MB E R 3 . M

BEHAVIORALSCIENCE

CONT ENTS

169 The Ecology of Isolated Groupsby Irwin Altman and William Haythorr

183 Subliminal Perception and Perceptual Defense: Two Sides of iSingle Problem by Donald Spenc,

194 Variables Affecting Decisions in a Multistage Inventory Taskby Amnon Rapopor

205 Honesty, Deceit, and Timing in the Display of Intentionsby Marc Pilisuk, Alan Winter, Rueben Chapman, and Neil Haa.

CRITIQUE AND COMMENT216 Validation Problems in Games and Simulations with Specia

Reference to Models of International Politicsby Charles Hermitid

232 Research Report234 Book Review

COMPUTERS IN BEHAVIORAL SCIENCE237 Legislative-Districting by Computer-SimulatiuiLZir^r; . _——" by James Thoreson and John Liitschwagei248 Computer Simulation of Change in Personal Belief Systems(

■

byKenneth Colbi254 Parant R^M,l.[miHlll*a.~m--triP P<.yrh.itrir Cnnn TTirtrary fiyon

System by Bernice Eiduson, Samuel Brooks, Rocco MottoArthur Platz, and Robert Carmichae

268 Computer Program Abstracts273 About the Authors

I

l!i

Reprinted from Behavioral

Science

Vol.

12,

No.

3,

May, 1967Printed in

U.S.A.

COMPUTERS IN BEHAVIORAL SCIENCELEGISLATIVE DISTRICTING BY COMPUTER SIMULATION1

James D. Thoreson and John M. LiittschwagerTexas Instruments Inc., Dallas, Texas, and Department of Industrial and Management Engineering,

University of lowa

The political districting process can be thought of as locating geographical boundariesthat enclose electoral populations. Equal population, as specified by the Supreme

Court,

suggests the use of quantitative procedures in districting that would draw district bound-aries on an impartial basis. Such analytical procedurescould unquestionably aid in achiev-ing equitablerepresentation on a population basis. The purpose of this paperwas to createrational districting schemes through the development of analytical techniques. The intentwas to provide an efficient means of generating many impartial districting arrangementsthat would satisfy both the equal population requirement and a number of traditionaldistricting criteria including contiguity, compactness, preservation of existing politicalboundaries, and singular representation.

This paper demonstrates that an abstract computer simulation model can be usedeffectively in creating rational districting solutions based on population equality. It alsoshows that additionalcriteria can be programmed into the modelto accommodate currentdistricting requirements. Moreover, the computational speed of the digital computerpermits rapid calculation and determinationof superiorarrangements.

C+3

The political districting process can bethought of as locating geographical

boundaries that enclose electoral popula-tions. In 1961 the U. S. Supreme Courtruled in Baker vs. Carr that federal courtshave jurisdictionto insure equality of repre-sentation in cases concerning state appor-tionment2 laws. Thus, states are currentlyfaced with the necessity of developingapportionment plans that satisfy federalconstraints; principally the "one man-onevote" population ruling in Reynolds vs.Sims. Thepurpose of this paper is to describetwo computer simulation programs that lo-cate district boundaries, using equality ofpopulation as the principal criterion. Partic-

1 Presented before the 1966 American Meetingof The Institute of Management

Sciences,

Feb-ruary 17, 1966. This study is also presented inThoreson (1965).

2 Apportionment refers to the allocation oflegislative seats among previously defined dis-tricts. Districting is the process of drawing thegeographic boundaries defining those entities.

ular referenee is made to districting examplesfrom the state of lowa.

CRITERIA FOR DISTRICTINGUntil the reeent Supreme Court decisions

many options were exereised in the district-ing of state legislatures. Some criteria thathave historically been incorporated into thedistricting process (see Harris, 1964, andWeaver and Hess, 1 96.3) include :1. Equality of Population—Each district

should contain a population nearly equalto that of every other district.

2. Contiguity—Districts should be com-posed of contiguous territory.

3. Compactness—Districts should be com-posed of compact territory. Although aprecise definition of compactness is notgenerally accepted, in a geographical con-text it means closely united so as toeconomize space.

4. State Law—Districting must adhere tostateconstitutional requirements.

237

Behariorar

Science,

Volume

12,

1967

Computers in Behavioral Science

2139

Computers in Behavioral Science238

5. Homogeneity-Districts should—ncommunities of common economic and « districts were to conformsocial interests, and allowance should be w^ast preservation ofmade for other ecological and psycholog-ical factors. subsequentlypassed by the 60th

6. Singular Representation-There should J^^^gl^it failed to gatherbe one and only one member representing {^^J^su'pport and was re-each district. Boundaries- jected when submitted to the people at a

7 52S£ S^SSr&-- -r---^r As-existing political regions, such as count s, As a co-equen

gegsion totownships, or cities. Legislators fre- sembly lequ

legislation. Thequently ask that district boundaries be enad new apport S

drawn so that seats of incumbent mem-the 61st General Assembly

bers are preserved.

hn,,

mWries and for a so-called "permanent plan" of8. Natural Features-District boumL«*» and «

temporaryshould follow geographic and topographrc app

introduced as a factor in the

features,

such as rivers or mountains. RationWhile population equality is now recog- Houge members. However, as a consequence

nized as the primary criterion, it does not tfae Reynolds vs. Sims decision following

follow that all of the remaining princrples ExtraordinarySession, the 61st Generallisted above are tobe overlooked m develop- Assembly in 1965 abandoned the permanentins a computer districtingmodel. Traditional and reviged the 59

_member Senate,criteria may be retained so long as the 124. member House plan, arguing that theresulting arrangement complies with poputa- genate pkn

wag

not apportioned on a

tion guidelines. population basis. Thus the most recent,„c T„ Tr>w. "terrmorarv plan" for future Assembles pro-

HISTORICAL PRECEDENTS IN lOWA J^^ Senate and a 124-In any redisricting situation it is difficult member House with no change made m the

to reach a solution acceptable to all parties .qus Houge plan- This temporarySenateconcerned since the political balance ot a .

g

digcugged in more detail later,legislature is usually at stake. Recent pro- f

61gt General Assembly (1965)posals of the lowa legislature rllustrate d aproposed constitutional amendmenttypical districting difficulties. that would limit tne number of members in

At the time lowa became a state in f»4O, and Houge to a maximum of 50the lowa Constitution essentially provided m respectively . Population equalityfor apportionment of the legislature on tne compactnesS) contiguity, andbasis of population equality. However, in miggive Bubdistricting to achieve singular1904 and 1928 amendments to the lowa resentation are prescribed for each legis^Constitution altered the laws governing the body Particularly; the preservation ofcomposition of the legislature. These amend- wou,d nQ longer be a require-

ments provided that the House of Represen- ment In order

tQ

become effective thistatives primarily involve area representa- amendment must subsequently be passedtion, while Senate apportionment was to be (unaltered) by the 62nd General Assemblydetermined, at least to some extent by and by the people at a popularpopulation. This policy remained in effectuntil 1961 with only minor changes. Current litigation is centered around sub-

The 59th General Assembly (1961) began dividing diatricts with more than one legis-the reapportionment process by enacting a 3

Behavioral

Science,

Volume

12,

1967

DEVELOPMENT OF ANALYTICALPROCEDURES

Equal population, as specified by theSupreme Court, suggests the use of quantita-tiveprocedures in districting onan impartialbasis. This paper presents two such analyt-ical techniques. The intent in both is toprovide an efficient means of generatingmany impartial districting arrangementsthat satisfy both the equal population re-quirement and a number of the other dis-tricting criteria.

The approach taken in both methods wasto simulate rational districting logic with anabstract model. The model was then pro-grammed for use on the University of lowaIBM 7044 digital computer, the speed ofwhich permits rapid generation of manyalternative arrangements. Additionally thecomputer permits rapid calculation and tab-ulationof population measurementsconcern-ing each simulated districting arrangement.Based on these quantitative measurements,the more equitable plans can then be selectedas the better districting arrangements.

A prerequisite to the application of theapproach discussed above is a decision ofhow to measure population equality ration-ally. A second prerequisite is how to repre-sent quantitatively other factors such ascontiguity and compactness.

Measurement of population equalityOne quantitativemeasure used toevaluate

population equality is the "populationvari-ance ratio." This measure is defined as theratio of the largest to the smallest populationper representative.

A second measureis the minimum percent-age of the states' citizens that reside indistricts electing a controlling majority (seeSilva, 1965). This percentage is obtained byranking the districts on the basis of theirpopulation per representative from smallestsubdistricting multi-member districts, such thatsingle-member districts can be put into effect forthe 1968 elections. An April 15, 1966 ruling of thelowa Supreme Court held that subdistricting isnecessary to extend "equal protection of thelaws" to all citizens. This decision was thenappealed to the U. S. Supreme Court which onOctober 10, 1966 refused to review the lowaSupreme Court decision.

Behavioral

Science,

Volume

12,

1967

to largest. The population of each succes-sively larger district is summed until amajority of legislators is accounted for. Atthis point the summed population is dividedby the state population yielding the mini-mum percentage figure.

The allowable deviation from "perfect"equality is notfixed. However, the AmericanPolitical Science Association Committee(1951) has recommended that a districtpopulation should bekept within ten percentof the state average, and in no event shouldit exceed 15percent.

Bonfield (1964) contends that the Courtsuse both the variance ratio and percentagecontrolling majority in their interpretationof population equality. Table 1 summarizesrecent Supreme Court decisions studied inthe Bonfield paper. The rulings appear to bebased on unconstitutional deviation fromequal population, since the courtrejected allplans except the one for the Colorado House.While the Supreme Court did not render adecision on the Colarado House (since itfound the Colorado Senate unconstitu-tional), it did note that the Colorado House"is at least arguably apportioned substan-tially on a populationbasis."

The Bonfield paper also discusses lowapopulation statistics for temporary and per-manent reapportionment plans passed bythe 60th Genera] Assembly (1963). Basedon population the temporary and perma-nent arrangements for the House hadpopulation variance ratios of 2.2 and 1.8respectively with percent controlling ma-jorities of 44.8 % and 45.8 %. The temporarySenate plan, with a variance ratio of 3.2and apercent controlling majority of 38.9 %,was much more equitable in a populationsense than the permanent Senate plan. Asa result of its interpretation of theReynoldsvs. Sims decision, the 61st General Assem-bly (1965) passed a new temporary Senateplan with a ratio of 1.7 while the previousHouse plan with a ratio of 2.2 was unal-tered. As will be shown subsequently, plansgenerated by computer simulation can befound that further reduce these ratios.

In all the above, total population hasbeen assumed to be the appropriate popu-lation measure although various other

Computers in Behavioral ScienceComputers in Behavioral Science240 241TABLE 1

Supreme Court Rulings

State

5.0:1 43.0HouseAlabama

New York Assembly 12.7:1 37.5Senate 2.6:1 38.1

4.3:1 40.52.6:1 41.1

HouseSenate

Virginia

1.7:1 45.13.6:1 33.2

HouseSenate

Colorado

measures have beeir proposed including thepopulation of voting age, population ex-cluding aliens, and registered voters.

ContiguityOne of the hazards encountered in ana-

lytical districting proceduresis theformationof enclaves. This phenomenon occurs whenthe formation of a district isolates one orseveral population regions. Normally thelimited population of the trapped regionsprohibits their being formed into an accept-able district. If the creation of enclaves isnot prevented, the final districting arrange-ment will likely violate the contiguity orequal population criterion or both.

The principle of contiguity is generallyconsidered essential in thc development ofdistricting arrangements. Quantitativemeasurement is unnecessary because thereis no concept of "best" contiguity. A dis-trict is either contiguous or not.

Compactness measuresCompactness is generally used as the

third principle of scientific districting pro-cedures. Together with equality of popula-tion and contiguity, compactness limits thenumber of possible solutions. Various at-tempts at quantifying compactness havebeen discussed in the literature.

Harris (19(54) applied a trial and errorsolution technique in arranging Congres-sional districts in Colorado. For a compact-ness measure he minimized

_t\L,-Wi\i-i

Behavioral

Science,

Volume

12,

1967

T .... Population PercentLegislative Variance Controlling

aoay

Ratio MajorityLi = the maximum length of district iWi = the maximum width of district i

and where the parallel bars indicate abso-lute values. An optimal arrangement usingthis measure wouldrequire that all districtsbe circular or square.

A similar formulation mentioned byHarris and also by Celler (1952) wouldminimize the sum of district lengths dividedby the widths. Although the optimal dis-trict shape would again be circular or square,this measure does not account for the dis-proportionate area between districts asdoes the previous criterion. For example, adistrict with dimensions one mile by threemiles is by this definition identical in com-pactness with a district 100 miles by 300miles. Normally the former district is con-sidered more compact.

Hess and Weaver (1965) use a minimummoment of inertia concept together withinteger linear programming for solution ofDelaware redistricting. This suggests a con-ceptually different definition of compactness.Whereas the Harris procedure measuresgeographic compactness, the moment ofinertia method is a measure of the dispersionof population about the center of popula-tion.

The programs described in this papermeasure population equality using bothpopulation variance ratio and percent con-trolling majority. The Harris measure, sumof maximum lengths minus widths, is usedas the main measure of compactness al-though the moment of inertia statistic isalso calculated. Each of the two methodsdeveloped (hereafter referred to as Methods1 and 2) requires that the state be dividedinto population regions for which censusdata are available. Districting is then ac-complished by forming combinations of theregions. While data and constitutional re-quirements peculiar to the State of lowaare used as examples, the programs couldeasily be applied elsewhere.

Method 1 is most suitable for a limitednumber of population regions such as the99 counties in lowa. It is particularly ap-

plicable in situations where regions are notuniform in size or when political boundariessuch as county lines must be preserved.Extensive use of this method has shown itto be an extremely rapid and flexible tool.

The primary advantage of Method 2 isthe greater number of population units thatmay be included, such that more equalpopulation representation is achieved wheremajor political boundaries need not be pre-served. By decreasing the population perunit, the likelihood of improving populationequality among districts is increased. Thcmethod assumes that the population unitsare approximately uniform in geometry. Inlowa the nearly 1600 townships are approxi-mately uniform and may be used withoutserious difficulty although the use of censusenumeration tracts in some would still bedesirable.

Following the presentation of each of thetwo methods is an illustrative example. Theexample following Method 1 uses countydata as the smallest population unit whilethe Method 2 example uses township data.The resulting plans from these examples arethen contrasted with the temporary reap-portionment plan passed by the 61st Gen-eral Assembly in 1965.

METHOD 1The method presented in this section is

an expanded implementation of some basicconcepts discussed by Vickrey (1961). Themethod presumes that equality of popula-tion, contiguity, and compactness are themore important districting criteria. Addi-tional criteria such as singlular representa-tron and preservation of existing politicalboundaries may also be included. It is alsoassumed that the number of members to beseated, k, is known.

This method is divided into two parts,l^hase 1 and Phase 2. Phase 1 provides asingle-member district where possible. Theoptional Phase 2 procedure consists offorming successive two-member districtsusing two single-member Phase 1 districtsto further reduce inequality of representa-tion.

Phase 1 algorithmA description of the Phase 1 districting 2

algorithm follows:

Behavioral

Science,

Vorume

12,

1967

1. Partition the state into regions withtheir enumerated populations. Definean arbitrary coordinate system and foreach region approximate the pointlocation of the geographical center. Theregions may have different shapes. Themost densely populated regions shouldcontain a lower population than thestate average per member. If someabove average regions are used, step1a must be performed before step 2.

la. In cases where a region contains apopulation above the state average permember arrd where subdistricting is notpermitted, seats must be apportionedaccording to previously establishedrules. One such rule, Option 1, would beto assign to each above average regiona number of members found by dividingthe region population by the stateaverage population per representativeand rounding the result to the nearestinteger. If the application of this sim-plified rule adversely affects the popu-lation variance ratio, a more complexrule may be applied. A second rule,Option 2, might be as follows:A. Regions with populations greater

than or equal to a preassigned lowerlimit and less than a preassignedupper limit are assigned a singlemember.

B. Regions having populations greaterthan or equal to the upper limit andless than or equal to twice thelower limit are combined with addi-tional regions to form a two memberdistrict following procedures similarto step 4 below.

C. Regions with populations greaterthan twice the lower limit are appor-tioned a rrumber of members equalto the population of the regiondivided by the state average,rounded to the nearest integer.

(In our programs Option 2 has onlybeen utilized with the Phase 2 proceduredescribed below.) Continue assigningmembers as above until the remainingregions are below the lower limit.Choose an arbitrary reference region;call this region Ai.

wheren = the number of districts

Computers in Behavioral Science 243Computers in Behavioral Science242

3 Determine which of the unassigned preserving county boundaries often produce' regions is farthest from Als call this Bx. Phase 1 districting schemes that are not

4 Starting with Bi assign to the district deemed sufficiently equitable on an equal

contiguous unassigned regions in order population basis. If the criterion of singular

of increasing distance from 8,. Continue representation can be abandoned, a secondaddfog regLis using the above dis- stage or "Phase 2" procedure may betancecontiguous criteria until an addi- adopted to reduce the inequalities. If

tional combination would exceed the adopted, this procedure ,s apphed at the

"population quota." Population quota completion of step six of the above opera-

at each stage is the ratio of unassigned tional procedure.population to unassigned seats. At this In Phase 2 successive two-member dis-joint abandon the distance criterion tricts are formed usmg two single-member

and select the contiguous region that Phase 1 districts. One district m each corn-will minimize the absolute deviation bination is currently involved in the popula-

from population quota. If the resulting tion variance ratio. Thus, with each corn-total for the district is below quota, bination the ratio term rs reduced. Theonce again select the contiguous region Phase 2 procedure is terminated when fur-

that will minimize the deviation from ther reduction in population variance ratio

quota This constitutes <an election dis- would require three-member combinationsquota. or viokte the requirement for contiguity.

5 Repeat steps 3 and 4by finding which The Phase 2 routine presumes that the

of the present unassigned regions is two members in the combination are elected

?arthest from A.. Call this B„ and form at large. Thus, average population per

a new district about B_. In general, member can be used as the population

new districts are formed about B<, i = represented by each member.1 , 2, "" " , until all regions and members Discussion

_Method ,are assigned. Completron of thrs step "l

,,.,

u- t aconstitutes a districting plan. The likelihood of enclaves being formed

6 Calculate the statistics associated with is reduced by moving from the extremes of'

the completed arrangement. These in- the state toward a fixed point. This reason-

elude quantitative measurement of the ing is reflected in the selection of B,- farthestpopulation equality and compactness from A.v ? . The procedure presumes that some differ-

-7 The^arrangemcnt of tbe districts is ence in the population of the final districts' dependent upon the choice of reference is to be tolerated. The possibility that the

region Ain step 2. Although the selec- excesses or deficiencies might accumulate,

tion ot a few reference regions may leaving excessively large or small resrdualresult in identical plans, generally the population in the last district, gives rise to

resulting arrangementswill differ. Thus, the "quota" term. By recalculating quota

repeat the districting process (steps la after each district is established, in terms ofthrough 6) by selecting alternate refer- the remaining number of districts and the

ence rerions Ax. Continue forming new remaining population, residual populationdistricting schemes until each region is controlled. In effect, this "floating aver-

has been used for reference or until age" tends to correct variations as they

sufficiently equitable districting has occur. .been achieved! Select as the final ar- The formation of districts about B* rn

rangements those plans most acceptable order of increasing distance tends to formon the basis of population equality and "compact" districts. Although distance is

compactness measures. partially abandoned m favor o populationv equality and contiguity in selecting the last

Phase 2 algorithm regions in each district, the resulting loss in

Normal districting difficulties com- compactness is relatively small.pounTd by constitution restrictions such as Concerning step 7 our computational ex-

Behavioral

Science,

Volume

12,

1967

perience has shown that the formation of aPhase 1 plan for each point A can be ac-complished in well under a minute on theIBM 7044 depending upon the degree ofsubdivision of basic population data andthe number of members to be seated. Thus inour runs we were always able to use each ofthe 99 counties in lowa as a starting Avalue. The Phase 2 procedure, when used,requires a longer time since data reorganiza-tion is needed after Phase 1 to insure con-tiguity iimong the newly formed districts.Our more limited experience with thesetimes indiciites a requirement of about 20to 30 additional seconds per plan, utilizinglowa's 99-county data in allocating 61seats.

Sample problem—Method 1The illustrative example involves the

solution of a (il -member Senate plan for thestate of lowa. Although our experience indi-cated that numbers other than 61 yieldedmore favorable plans, this number waschosen to conform with the number in thetemporary plan passed by the recent 61stGeneral Assembly. In addition to the prin-ciples of population equality, contiguity,and compactness, other lowa districtingrequirements in effect at that time were:

1 . District boundaries must follow presentcounty boundaries.

2. A number of members may represent asingle county.

3. Those districts composed of more thanone county may include no more thantwo members.

lowa population data were gathered using1960 census material. The geographical

Behavioral

Science,

Voiume

12,

1967

center of each county was approximated foruse in the necessary distance calculations.

Two alternative solutions are presented.The first plan involves only the Phase 1procedure while the second involves boththe Phase 1 and Phase 2 portions of thealgorithm. These have been separated toillustrate a plan in which singular represen-tation has been preserved4 as opposed to aplan involving two-member combinations.Both plans adhere to therequirements listedabove. ThePhase 1 plan utilizes only Option1 of .step la of the algorithm.

We next discuss the statistics of the betterPhase 1 plans. Each of the 99 lowa countieswas used as an initial reference region in thesimulation of this districting problem. Thecriteria measurements associated with thebetter simulated arrangements are shown inTable 2. These 6 of the 99 simulated plansexhibited the lowest population varianceratio of 1.56. Ideal districts would have apopulation of 45,205. The plan resultingfrom using courrty 78 as a reference point.A was selected as "best" since it possesseda 46.84 value for the percentage controllinga majority vote, the highest such percentageof the 99 simulated. Further, the sum oflengths minus widths compactness measurewas the lowest of the 6 plans. Thus, based onthe simulated statistics, the Code 78 ar-rangement is clearly superior.

Other solutions used both the Phase 1and Phase 2 portions of the algorithm.Again all 99 starting points were used in the

4 Singular representation was preserved amongthosecounties containing a lower population thanthe state average per member. Multiple memberswere apportioned to seven over-average counties(Step la).

TABLE 2Six Phase 1 Plans

for

61 Members

Reference Region Code (A,)1 5 15 73 78 88

Largest population per memberSmallest population per memberPopulation variance ratioPercent controlling majority voteSum of lengths minus widthsMoment of inertia measure (XlO6 )

55060552821.56

16.52i0.792.24

5506035282

1.5646.5848.22

2.15

5506035282

1.5646.4852.352.19

5506035282

1.5646.4048.082.18

5506035282

1.5646.8439.462.10

5506035282

1.5646.5650.072.04

Computers in Behavioral Science Computers ix Behavioral Science 245244TABLK

;i

element A. Although n few referenceAssignment Sequence elements will result in

identic*!*!

nlnns" i*■ f^onmWpm ThpCode3plan 1. Place a grid over a map of the state so TABLK 3 element A. Although a few referencesrmula ion of that the entire state is contained w.thtn Assignment Sequence element, wiU result in id(> ntical plailß>was selected based onf 0ex. the grid. Each cell of the grid should be ; - ...........

arrangements wilvariance ratio Whereas *e Prev^s ex geometrically uniform as possible. The * oc„r Order „,

A,,

gllimil differ f £ £ample resulted in 6 pUn j ej gshQu]d such m „

4{J £S^STl^^ Illation exceeds the average popula- ,

■

refei,n,e lemonts A

It Juldbeno^dth^^exhi^ i^ri,J, £ £ tTiLS^tS^^SorciSZ?"l valSl^^tty accurately as possible. (Harris 1964, . , or until suffi(,ently (,quiUlbl(, distm, inß.a i tnl4nthnmEhasPhase2combina- discusses several approaches for est.- Vl[ 18. 1?! io] l« Has been achieved. Select as the finalreaucea ro iw s * mating population densities.) VIII 22,21,23,20,24 arrangements those plans most aceept-*^-^fao^* n^^3X 3 Using the grid cells and their assocated _

able on the quality of population cri-creased.the^Percentage controllinga majorityform a population teri()n , f Jlaiw 'i d infrom 46.5% to 46.9/o. IP

subsf;r

ipts to each sHected ,„ the order of increasing dis- popuiution equillitv> ,lm()H0 tlu! most

Vector Order of Assignment

I j ess, steps 4 through 8, by selecting alter-

VI 14 13 15 io or until sumeiently equitable districtingVII

18,

\l\ 1!)' 16 has been achieved. Select as the finalVIII 22, 21, 23, 20, 24 arrangements those plans most accept-

].,, , , . . tenon. If several plans are equal inselected in the order- of increasing dis- i

.■

i*. i a,, D , r A . population equality, choose the mosttarrcc from Bi. Ao member of the succeed- . „„ . + en Acompact of these.ing group is considered until all elementsof the previous group are assigned. Con-or tne previous group are assigned. Con- Discussion- Method 2finite assigning elements to the districtuntil the deviation from quota for theuntil the deviation from quota for the The grid arrangement of population units,succeeding addition would be greater subscripted to form a. matrix, permitsthan the previous deviation. This con- several simplifying assumptions for com-stitutes a district, figure 1 illustrates an puter programming purposes. First, the dis-

tances between elements can be calculatedexample formation of the grouping ar- tarrces between elements can be calculatedrarrgement for a single district. The cell using the subscripts for position coordinates.numbers represent the order- of grouping. In il strict sense, square cells are assumed.Table 3 lists the elements in each vector However, the distances merely locate cells

In a strict sense, square cells are assumed.

and their order of assignment into theand their order of assignment into the ''olative to other cells such that similar,district. although somewhat nonuniform, grids may

Normally Bx will be located in a cornerNormally Bx will be located in a corner hc UHC(I without loss of generality. Second,of the state or partially surrounded by contiguity data are defined by the matrixassigned elements. In such cases grouping subscripts. Thc computer program assumesand assignment continues as if the ma- thilt ( 'ells witn adjacent subscript notationstrix were extended. Since only unassigned are contiguous. For example, element (3,3)elements within the state boundaries are is defined contiguous with elements (2,3),

ments provide a practical limitation. thc summe(l population of the urrassignedThe method presented below was devei- e]ements exceeds the district population

oped to permit computer solution of prob- -^ Population quota is defined aslems involvinga larger number of population of unassignc(i population tounits. The criteria for this method are

unassjgncd members,populatiotr equality, compactness, conti- T<> f<)rm th(; distri(, t nrst assign Bv,guity, and singular representation. In par- (i()nsider the elements within eachticular it should be noted that the use ot fine

suc(,css jve vector. Add to thc district the

assigned to the district, the techniciue isassigned to the district, the technique is ("%-)> (4>3). and (3,4). Finally, it is as-unaltered, sumed that the subscripts may be used forelements within each

identification.Add to thc district the 7. Repeat the process, steps '■> and 6, byncuiai ii* ouuuiu

*av ..~

— . successive vector,

/^uu

w *.n^

**.*,*..

~~ —population subdivisions permits the crossing

mmsi-\„

nCi

\ elements within each vectorof larger governmental boundaries (such as

lowa county lines) in establishing

;

district ___— —-—y - , j

finding which of the present unassigned Deviation from a perfectly square gridregions is farthest from Ax. Call this B, is permissible to the extent that subscripts

Deviation from a perfectly square grid

provide accurate relative distances and con-and form a new district about B, utilizing12 I 11 10, 9

v274TT

VI V V V VIIIIUWiI UllUllUJ*

»""'/

■"■ - ...lines. The method presumes that the indi-

vidual population units have been definedby partitioning the state into a square gridarrangement. Minor deviation from thisassumption may be justified. Discussion ofthis point follows the presentation of thealgorithm. The method again assumes that

the number of members to be seated, fc, hasbeen specified.

tiguity. Violations of these assumptionspreviously unassigned elements. In gen-eral new districts are formed about 8,,

1f

8IV

23VIII13

VI2II

i =

l, -J, """/£,

until all elements and TABLK 4members are assigned. Completion of Summary and Comparison of Populationthis step constitutes a districting arrange- Statistics7

IV2214

VI3II

0Bi ment or plan.VIII

8. Calculate the statistics associated with Population Percentthe completed arrangement. These in-the completed arrangement. These in- Districting Plan Variance Controlling

■ . ,-, ■■ . Uatio Majorityelude quantitativemeasurements oi pop- _ _illation equality and compactness cri- Senate File 508 1.71 45.1teria. Method 1 Phase 1 1.50 40.8

21VIII4

1115

1116IV

15VI

17VII

18VII

19VII

2016VIIMethod 2 algorithm VIII

A generalized description of this algorithmFig. 1. Sample Cell Arrangement

follows:

Behavioral Science, Volume 12, 1967

Behavioral

Science,

Volume

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1967

i = 1, 2 ,■

■

-k, until all elements and

9. The arrangement of the districts is de- Method 1 Phase 2 1.40 40.9pendent upon the choice of reference

Mo,l

"H ' 2 !"■«> -*">■<*

METHOD 2

The resulting equality of population ofthe analytical methods discussed in thispaper is naturally somewhat dependent on

the population within the individual popu-lation regions. Deviations among the popu-lations of the districts may be furtherlimited by defining units with small popula-tions, say less than five percent of the aver-age desired. Such practice, however, in-

creases the total number of populationunits required. While the operational proce-dure of either method does not limit thenumber of units, computer storage require-

element.4 Choose an arbitrary element of the

matrix for

reference;

call this elementAi. .11

5 Determine which of the unassigned ele-' ments is farthest from Av, call this ele-

ment Bi.6. With Bx as a starting point, group ele-

ments about Bx into column and row

vectors such that combining each suc-cessive vector would result in a contigu-ous rectangular district. Let the firstvector consist of the single elementimmediately above £ Continue in a

concentric counterclockwise mariner until

Computers i.v Behavioral Science Computers iv Behavioral Science 247240

j!i

may cause the formation of noncontiguous equitable districting. It should be pointeddistricts <mt however that several of the data ele-

In general the discussion following merits contained populations in excess of

Method 1 is also appropriate for this 30,000. Obviously if these elements hadmethod However, the Phase 2 procedure is been subdivided, the variance ratio couldnot used with this method since the size of have been further reduced. A few of the b>othe matrix elements may generally be re- plans also formed small trapped populationduced until sufficient population equality is cells.achieved. This method assures singular CONCLUSIONSrepresentation in all districts. . ,i„ u

,.,.;k,wl

Computationally we observed an average Our experience utilizing the descbedexecution time of about 30 seconds per plan programs has shown that computer _s.mu.a-using lowa township data. tion can be used effectively in creating d,s-

-h tricting solutions based on populationSample problem Method 2 equality. Moreover the computational speed

of the digital computer permits rapid calcu-lowti townships conform sufficiently to iat -01, , uui determination of the superior

the above assumptions to permit their .ltTangements. It has also been shown thatbeing used as matrix elements/' additional criteria can be programmed into

Theexample problem using this algorithm fh(i mo(jo] t() accommodate local districtingalso involves (il members such that results m , u jrement,s.may be compared with all arrangements Table 4 illustrates the "temporary"presented. The solution presented for this Senat(, pi an actually passed by the 01 stmethod was based on the population statis- (j oneral Assembly. A comparison of thistics from a total of 100 simulated plans arrangement is nowmade with the computerusing randomly selected reference elements generated as a part of this paper,from the nearly 1000 possible townships. Th(> , _- (; aml ,40 varjancc ratios of the

The statistics from the program indicated otn()(i ) computer plans tire clearly su-that theplan associated with the subscripted ()r f(j the , 71 mtio exhibited by thetownship (4,7) was the* "best," based on I(,sis iators> piun) Senate File 508. Likewisepopulation. This plan exhibited a popula- th(> per(,cnt controlling ii majority vote intion variance ratio of 1 .30 and a percent ()f the

C

omputer plarrs is higher thancontrollingmajority of

49-.iV/;.

tn .lt ()f tno temporary Senate plan. Thus,More than 70rv of the simulated plans on t|1(1 nasis ()f popuiaTic)ii ecpiality the ccim-

had a variance ratio of less than 2.0. Using nter plans are m()re e(luitable.data elements containing relatively small Although the example township plan ofpopulations increases the likelihood that a \jethod 2 is not directly comparable, itssingle random starting point would result in poti(,llt ja] jn achieving further population

, , equality is apparent.>,„ lowa there are seven o a,.,m«>"g «, should b(1 n()t(,d thaf th(j phaH(, , form

1600 township popula ions that exceed

-l.i.zuo,

" .the avencge population for 01 members (1960 of .Method 1 of this paper Was used 1.1 sub-census). Linn, Poiiawai tamie, Scoii, Woodbury mittiiig districting plans to members ot theand Dubuque counties each contain one such (,l

ST

General Assemblv.township and Polk county contains two. In the

(ilst General Assembly.

sample problem data, an eighth over-average REFERENCES

element,

was formed in Black Hawk county dueto subscripting requirements. The algorithm for American Political Science Association, Com-

this method does not include an apportionment mittee on Congressional Reapportionment.this method does noi ineiuut-

an

n|*|a*"

**" ■■■■ """" *

-y -

»■

procedureto handle these over average townships The reapportionment ot congress. Amer.

since it is intended that finer population data be polit. Sci. Rev., 19.>1, 4.i, 153-1.)7.

used In this example townships in these counties

Bonfield,

A. Memo concerning constitutional

wereapportionedby hand to avoid gathering more limitations on state legislative apportion-

detailed population data by census enumeration ment with special reference to

lowa,

Un-

districts In total 10 members were apportioned published paper, University of lowa LawSchool, lowa City, 1964.

to the over-average townships.

Behavioral Science, Volume 12, 1967

Celler,

K. Congressional apportionment past, Thoreson, J. 11. Computer aided districting tech-present and future. Law and contemporary niques. Unpublished thesis, University ofProblems, 1952, 17, 268-275. \ tmii< IoW!l f*i* v , 1905.Harris, C.

C,

Jr. A scientific method of district- Vickrey, W. On the prevention of gerrymanderinging. Behav. Sci.

Quart.,

1904, 9, 219-225. l'om. Sci. Quart.. 1901, 07 105-110Mess, K. N., Weaver, J. R., Siegfeldl, 11. ,)., & Weaver, J. 8., .v Iless, S. W. A procedure forZitlan, P. A. Nonpartisan political redis- nonpartisan districting: Development ofIrictmg by computer. Opera! . Res., 1905, . computer techniques. Yale Law Journal13,098-1006. 1903, 711, 288-308.Silva, Kuth C. Reapportionment and redist rid-ing. Scient. Ameiitart, 1905, 213, 20-27. (Manuscript received March 24, 1966)

c-*o

Theproperties of the whole are, at least some of them, new, andin just this respect are a "law unto themselves," and in this sensefree. This does not mean that they tire lawless, but only that theirspecific principlesof "behavior" are not identical with those of theparts. . . freedom consists, therefore, of actions in accordancewith the characteristics which subsist at a certain level of organi-zation but do not exist at other- (lower) levels, yet it is quite com-patible with law and determination both at this level and tit lowerlevels.

E. G. SI'AULDIN'CI

Behavioral Science, Volume 12. 1967

Computers in Behavioral Science 249

i

COMPUTER SIMULATION OF CHANGE IN PERSONAL BELIEF

SYSTEMS1

by Kenneth Mark Colby

Department of Computer Science, Stanford Universilu

This paper deals with the following questions and answers: Are models the same as

theories? No. Can a computer program serve as a model of human belief systems? Yes

How do humans change their minds? We know little about it. How can models of behefsvsTems beCorroborated? By engaging the person whose belief system is being modelledinreputed on-line dialogues. Is it dehumanizing to use a machine such as a computer as

an agent for mental change? No.(T+3

Tlsychothbi.ap.es, whether individual or This root metaphor connecting mentalPgnmp onsist of a communicative ex- processes with computer processes.should be.tuige of semantic information between understood as involving not static entities,

. an attempt to alleviate those but comparableflows of information proces-

e ml n int rpersonal misunderstand- ing. An inert or idlingcomputer is just hard-; wiLh are involved in mental suffering, ware. But a computer run, -^imng hard-

f shall discuss some work in computer science ware and program into a single active sys-

wh£h is intended to contribute to the psy- tem, represents the correct analogyto human

i tmpt by increasing our understand- thinking. A computer", harmonizespower

in* of change in certain thought processes. energy, and information. It stands as anmg oi < nan^i » analog model with its symbolic' representa-

MODELS tions being processed in a physical mediumBv now it seems overworked even to say (computer) known to be different from the

th*,t the term "model" is overworked. But physical medium (brain) in which thought

models are'here to stay so we might as well takes place. The distinction between this

Bet used to repeated explications of them, type of model and scale models mathemati-Models themselves are not so much the <*al models, and theoretical models has been

trouble as the nature and consequences of well outlined by Black (1902).etiphor' Some theorists feel that mode,s and

111When we link two systems in a metaphor theories are the same. If so, one should just

we -ire inclined to sav "A is a B." We mean say "theory" instead of model Ihe useful

in an impliedcomparison that A, theprimary distinctions are as follows: A theory partic-

svstem is like B a secondary system, in at ularly in thebehavioral sciences, is stated in

least some respects. To sav human minds are the form of natural languagesentences Ihe

like computers is onlv to claim that they are sentences are strings of symbols which dc-

to be taken as alike in those ways which we scribe the properties oi a theory. A model, in

wish'to emphasize. We hope to increase our this instance a running computer programunderstanding of minds, something we do consists of strings of symbols which do not

h t know much about, by comparing them just state, but cremphfy * structure of thewith computers, something which, if we do system that a theory talks about No one

not know much about, we can readilyknow expects the syntax of theory sentences t«

much more about because of their properties exemplify a structure other than that of*

of tangibility, traeeabilitv, and manageabil- grammar of thc describing language. But'j . M "' a model is expected to embody a structure m1 J ' its symbolic representation. When we de-

' Paper delivered ia Section 1,., The Psychiatric

&

mol\c\ we are back to natural lan-

SSS M(S! .£:: ?t^ *»^ «**» »«* thi* »ften lcads to

248

Behavioral

Science,

Volume 12. 1967

a confusing of models and theories. Thesecrucial points have been made best by Kap-lan (1904).COMPUTER PROGRAMS AS MODELS OF

THOUGHTA computer simulation of thought pro-

poses analog models as an information proc-essing account of mental activity. They con-sist of ti conjuction of hypotheses about themost relevant variables, some of which areinferred and some of which a person can ob-serve within himself as well as in the utter-ances of other persons.

The mind-computer metaphor suggeststhat mind is like a program running in acomputer and that the central nervous sys-tem in humans provides the physical mediumanalogous to computer hardware. The"ghost-in-the-machine," so troublesome tomind-brain theorists such as Ryle, is simplya running program. Themind-brain impassehas awaited a suitable root metaphor withwhich one could do something in substantiveand satisfactory detail.

In attempting to account for the intro-speetional reports of persons, programs arecomplex enough to satisfy our notions re-garding the complexity of thought and theygenerate symbols at the proper level of concrete detail to match the output of persons.At the end of a run not only can the outputstream be examined, but one can retrace infull the step-by-step operationswhich gener-ated this sequence. Programs are speculativeinstruments, strategic simplifications or"under-complexifications," whose remoteconsequences can be tested by running themthrough.

Models must be built for real situationstoo complex to manage directly or for tech-nological situations which cannot be experi-mented with during their operation. Oneshould not experiment with a bridge whilebuilding it or with a human heart whileoperating on it. The psychotherapies repre-sent applied technologies which are difficultto experiment with because of the largenumber of uncontrolled and nonindependentVariables. However, models of the processesbelieved to be involved can be constructed;«id tinkered with in order to develop theif-then generalizations and law-like state-ments typically yielded by controlled experi-

behavioral

Science,

Volume 12, 1967

mentation. Initial states can be repeated orvaried and alternative inputs can be tried toestimate the relative contributions of in-ternal state variables and external input tofinal output.

. THE PERSONAL CALCULI OF BELIEFSYSTEMS

Severalprograms have been written whichsimulate belief systems (Abelson and Car-roll, 196'); Colby and Gilbert, 1904; andColby, 190")). In this essay I shall summarizesome work from our laboratory and describethe directions of our current efforts.'2 ' 3

Details of the actual programs can be foundin Colby and Gilbert (1964), and Colbyand Enea (1907).

We define a belief as a basic molecularunit of symbol processing. It is a propositionaccepted its credible by the system. A beliefis represented in the program by a string ofwords in an internal English-like language,for example, "I like Al." The string is asemantic kernel sentence which can be ex-tracted from a natural language sentence. Asemantic kernel in the internal language isthat which remains invariant under combi-nation in natural language.

The atoms of a belief molecule consist ofconceptsabout interpersonal relations organ-ized as directed graphs; that is, nodes con-nected by pointers which indicate interrela-tion between concepts. Concepts are linkedto form beliefs which state propositions,judgments, or attitudes about persons, in-cluding the self, and relations between per-sons.

In addition to this semantic component, abelief has three assigned weights: a credence,a fixed charge, and a current charge. Cre-dence represents personal subjective prob-ability or credibility, the degree to which abelief is held as true. Charge represents thedegree of import or interest a belief has inthe system. The fixed charge is a morestableweight changing very slowly over time, while

2 These investigations are supported by GrantMil O<M)45 from the National Institutes of Health.3 I am grateful for the collaboration of John

Gilbert,

Center for Advanced Studies in the Be-havioral

Sciences;

James Watt, Horace EneaMichael Levitt, John

Gilman,

and John Sauter|Stanford Computation

Center;

Janet Kreuter'Department of Psychology; and Jane Burton'Department of Philosophy, Stanford University'

Computers in Behavioral ScienceComputers ix Behavioral Science 2r.l250

( .um!nt charge fluctuates more readily £t, )rding to the immediate fates of a bei e «st. ct« >* . whi(.h repre.The status of a belie m the "-t >

( £;» in

ft

pers(ma , b diefpr()du(,t of its credence a.ul its current hcalthy or pathologi-charge. , f,l i„, "'.i . irP n„t nronertics of an individual belief

A belief may or may not he supported < * . c n t pr >pc

reasons which consist ot other beliefs. Be- hut of abe et eU.^

lief's gain their position ot becoming rea- Given h *rprocesses, the

sons by being warranted by a more general confl s nd sd *^; ,f ,'„. b(>

SS ;^^-ternal (,>r;e( ,iveinp,,treason for "I criticize tathei is because nt pc * ()f inf()rmilt.ion to afather rejects me," which ,s warranted by the tron a. ot^cr^ <>f nnmmmi^implic-atiorr serving as a subst, tut.on rifle * ' ?hange woukl involve a >e-"A parent rejects a child implies a chid Idcallv, sue n a fe

wren,hing conflictcritics tt parent." These imp.icatjons aso u^ion^ woukl lead tohave credences and charges anc theyare be-^^ misbeliefs and miscon-

» , W d

«>

we change our

tion PDP-1 Both the 7090 and BDP-1 con- minds?nect to an IBM 1301 desk file which permits mjnd

use of an extremely large data base during

on-line experimentations using teletypes and Bridgman said, "There is some tunclamen-display scopes of the Stanford THOU time- tal incptncss in the way that all ot us handlesharing system. The program first forms a ()Ur minds » (1959). Wc really do not under-

pool of relevant, belief's around a nucleus stand bow to handle our mrnds properly ;_mwhich is the highest charged belief in the p.irti( .uiarj wc do not understand descrip-

system at that moment, Then, successively, tively how mi„ds clumge, or even norma-the highest, charged belief in the pool be- tively now they should change,comes the regnant and it is matched against There is plcnty ()f evidence that our minds,the others in the pool in a search for conflict. , ls indi( .ated bv our belief's, change during a

Two belief's are considered to be in conflict lifotimc . A belief which once had great im-

when they involve the sameor similar agents p()rt

_.

**santa Clans is coming!"-- no longer

"md objects and when action verbs are d()0S A belief

OIU

.(, held with a personalantonvmicallv related, such as "T hate pl,)babilit,y close to unity- "frogs cause

mother" and "si child ought to love a wlirtH»_ n()W brings only a smile. Also it is

mother "Tf no conflict occurs the regnant evident tntlt „ther persons change our

is expressed as air output belief, along with bdiefM_ Kev experiences with other personsthe values of monitors which keep track of

( .h.u ,se individual beliefs and even entire

how well the system is functioning at the belief HtrU(.t,ures. In this sense we can writemoment, A belief relevant to the output be- on ()ne mother's programs during that single

lief is then selected to take its place m the irreVersible pass through the world called

pool. If high-level conflict occurs, various i ife Weremake and revise our own programstransformations of the semantic component (.ontinuously, mainly in quite small steps

of the regnant are attempted until one is ex(,ept for key experiences and mostly vvnthinfound which creates a belief not in conflict character, that is within certain boundaries,

with other beliefs in the pool, and which Patients irr psychotherapy report that*

hence can be expressed. The transformations they have changed their perspectives and

ci 1^ he agents, actions, objects, or com- theirwaysof lookingat old facts about them-

mXn < t hes* bv computing suitable selves and others. While psychotherapists

It^fnm the directed graph of a have some fair, but incomplete theories

Behavioral Science, Volume

12,

1967

about, origins » mental suffering, they have deuce is lessened. This in turn may affectonly the crudest _ notions about mental thecredence of a belief for which it can servechange. As a practitioner, a psychotherapist as a supporting reason. If the main belief-is a decision maker using heuristic rules of serves as a reason in some other belief'sthumb which tell him what to say and what reason structure, and so on, the result is *inot to say in certain contexts. He needs a series of small but far reaching chain reac-vanable or set of variables he can influence tions in the degree to which some beliefs areto change and he is interested in facilitating now accepted as true. Preliminary resultsor accelerating mental change which occurs indicate that input designed to weaken thenaturally m human minds, from clinical reasons for a belief is moreeffective in chano-experience he knows that the rate at which ing the program than trying to weigh evi-corrcctive inputs can be generatedand com- dence for and against the belief directlymunicated by a therapist and the rate at In this program, since severeconflict 'leadswhich they can be accepted by a defensive, to belief distortions and conflict is a functionontogenetically battered system, must be of both credence and current charge we aremeasured incalendar rather thanclock units, also trying to posit mechanisms for reducinggiven our present state of knowledge. the charge on beliefs. It is not clear to us 'vetA belief system combines external input how these parameters interact or should'in-w.th stored information in accordance with tcract, but over time credences should prob-<"urre.it values of state variables and param- ably change more rapidly than chargeseters ot a personal calculus. Input informa-tion from another person must first attain CORROBORATIONsome interest and second, it must be evalu-ated as credible to some degreebefore it can Added to the number of difficulties inbe accepted into the system as one of its wntlnS Ulld running these programs is theelements. Belief systems in adults tire noto- f)roblom of validating or corroborating theriously conservative. Paradoxically they are modeL As

*vct wc h;lvo ""<> satisfactory wayof

■also gullible. Given a trusted source (if in- teHtll1K models of belief systems for theirformation, almost anything it reports is ac- eorrespondence with empirical observationseepted as having credibility unless there is "" t )ol'Hons boinS simulated. Since the pro-counterevidence against, it, 'Among persons gmm Klmulates an individual belief system,saying it does tend to make it so at least iii °nc W(,uld llke some meilns ()f eomparing itother* belief systems, when counterevidence W , ' )Utput from tho "'dividual person i„-cannot be marshalled and when the source vo'v.ed-is believed to be trustworthy We are developing an on-line method for

Through self-observation" one can observe te» tm& the model by enlisting the researchmoments calle<l reflective when we engage in eooperation <>f a person whose belief systeman internal dialogue designed to change it

belnS emulated (Colby and Knea, 19(57).belief or belief structure. We bring into play , l)roSmm tries to build up a cognitivea personal logic in attempting to dislodgeor m.)del of il porson nuu'h in th(>way apsychia-renounce a belief. A belief can be changed , ("°"HtrU( ' tH iUld refines his tbought-hy changing its semantic content, its degree nM)del of J . *.

)at|ent. A Person sits at a teletypeof credibility, or its degree of import, and Part-|('ipates m a continuous written

We are currently trying to simulate theseduU(,Sue m natural language regarding his

processes by an on-line dialogue with the dose '"[erpersonal relations. Each of theProgram which first attempts to lessen the "iltl"'al language input sentences undergoescredence of a belief by weighingevidence for fP« f" IV('Jlu ]um »"»I.V«-« in an attemptand against it, This is achieved by consulting ''' f tl'" M I'1?1^Tah the relevant beliefs in the system to n e n^ As. ?W ]rc"m H bd 'ds ,n«dcweigh evidence for the contrast of a belief. "±\Tf' l'nmm ">rf . , . ll()ns arc made to it graph representing be-" more evidence is found for the contrast li efs When

«ir,,„ h,.!,^

v,

,, \

t, . .

■* ,.«

vvnen some beliefs have been accumu-Uidn the original, the original belief's ere- lated the program then asks the person

Behavioral

Science,

Volume 12, 1967

kComputers in Behavioral Science Computers in Behavioral Science2")2 253

whether or not it is correcUy representingcertain of his beliefs. He rs free te co,rfjnn f^ and mcreasing iluUvidualreject or express ambivalence As m ps> i iea

■

k of cha ;chotherapeutic practice negate u, much au <«/-^ beliefs ,more ambiguous than assent, and the pro misbeliefs and misinformationgram must decidewhether to revise its repre- repeated childhood experienceMentations of the person's beheta oi■ whett,« aTd other key persons whothe semantic area ,s so conflictive as to give fn>m mu." H^v^'^'cooperation of a person in real current one.time and maintaining a continuous and re- I\*W*" > munpeated on-line dialogue, we are trying to paintybuild up and progressively -P-e of their symbolic representa-of that person's belief system. 1his tjpc <>i

Granted that some illusions are neccs-man-machine communication gives the t ns.

{ numbcr of s(),ialmodel a continuous interaction atclose rarrge JJ^J1j be reduccd ifwith the system it is representing and gives nusu dcrfctandi g

it a chance to be improved through new rn- peopte*^ (^/misbelieft But ,hangeformation obtainedby immediate feed-backs »^'^threat ,dH wc]l a8 opportunityfrom a person. But thrs type of man-ma- a* * "ph challengeH fu.id.i-chine ..ooperation and communication will

tne mlture of man. If ain the rapidly approaching third generation mentaJ" is takeu scri ouslyof computers, accentuate the growing rssues m " work at it, one of theof man-machine relations. The srtuatron will enough

>^ . t() be 1()()ked ttt inbecome particularly acute once a compute

(.nZ^ machines. Metaphornow

AS B^SVL ££»not only the hub of it but the rub

with whom it is «'»^7l^t jnf ■ fJ me be treated, like machines, as scrme-computer be a more powerfu 1 mfl uen^er o a menw

(, omparison sperson than another person? <\ ""^ sm *as inviting dehumanization. It hits ob-function as a change age, i l»s « Jte {)

_iipy? (See Colby, Watt, and Gilbert, 196b.) ~^s machine-like, for example, com-

THE MODIFICATION OF MAN paring the heart to a pump. The better ques-

»„„„„»,&„,» ,*.,*■! »ri.* aW ***** "^^Vph'ttaficriptta h» Kiv**.*

ir^"«^'"■£% :;^^p^J^ *"■tiwams .i yui. vv yl

wllh, tb,lt he in a man-machine metaphor.this assumption of perfectabflity that ne in ama , supposed that we arewishes to liberate himself frombeliefs judged Butiti r uf^^(

■ hto be too heavily weighted or inapproprrate very clear a machr 1 i

t„ contexts of a changing social and physical bec^e^n see t^ ,environment. .-md their implications

The stored past of a belief system may nature ot "^ is beiZLrify ourover-influence it in attempting to deal wrth and now all we "**"£"\Vot if wcnew information from new situations. Clm- concepts about the nature

take a computer as a primary system, it is in understaffed hospitals, we have no choicevery unclear what ,s a suitable secondary but to use it because the healing professionssystem with which it can be compared. As are unable to supply sufficient manpowertonew machines are invented the meaning of meet this great social need. If a computerthe term machine" changes radically. can teach children better than teachers canlo Descartes a machine was a system of because each child is thereby enabled toropes and pulleys. To my grandfather a learn at his own rate, then its use is quitemachine was what we went for a ride in on consonant with our values of respecting irr-Sunday afternoon. To me a jet airplane is a dividual dignity and promoting the growthmachine. A computer is a kind of machine of autonomy. To me it is dehumanizing towhich does not ook like these and does not put a child in a class with 40 other childrendo the same kind of work For a programmer and one teacher. It is dehumanizing tc. herda computer rs simply a device for processing thousands of patients into mental hospitalssymbols which in turn stand for his concepts where they will never see a doctor If *iabout some subset of the world. His most computer can teach and if a computer candirect concerns are not with hardware, but provide therapeutic conversation then therewith programming languages and how best can be ire. hesitation in exploring theseto represent his ideas in them. Ultimately, potentials. It mav give us a chance to rehu-when the program runs these ideas will be manize people now being dehumanized byrepresented in the medium of hardware, and our educational arrd psychiatric systems "naturally someone must worry about keep-ing the hardware

fit,

For the programmer's REFERENCESpractical purposes, a computer as a primary Abelson, 11. P., &

Carroll,

J. D. Computer simula-system can be compared to a language as a tion of individua> belief systems. Amer.secondary system. This metaphor then as- Black* M^'aS.' "' ?T v v ,„.j„il,( .. ■ i ... "lack, M. Models and metaphors. New York*serfs that a computer is a language in that it Cornell University Press, 19(>2.provides a system of symbols and operating Bridgman, P. W. The way things are. Cambridge,rules which we mav relate to our concepts Mass.: Harvard University Press, 1959.If man, as part of his cultural evolution de- Co,by

' K ' M

'

c»mP«tf simulation of neurotic.*» h.

* .,<■

*,», „„„,-,,„; ,e rs:r?„:,;:z i-i_t__i w:=:seems to have done so—then the problem is New York: Academic Press, 1965.whether he can do so aided by a machine Colby, K. M., & Enea, H. Heuristic methods forsuch as a computer. Can the study of man- computer understanding of natural languagecomputer communication improve knowl- T^edge of man-man communication and its Colby, K. M., &

Gilbert,

J. P. Programming amisunderstandings? computer model of neurosis. J. math.Bv itself a computer does nothing. It is a Psychol., mu, 1, 405-417.

conceptual and technical aid to a person's C"lby ' K/ M" a"' Jk' * (* i,bcrt- J - p - A c"m-

tV,;.,

l.- ii " lr

■,

, . , puter method of psychotherapy. J nervthinking and knowing. If it can be used to menL

a;,*.,

toco, ui, 148-152further our understanding and treatment of Kaplan, A. The conduct of inquiry: Methodologyformental suffering there can be

HO

questionof behavioral science. San Francisco: Chandlerits value. If it can be used as a psychothera- I'ublisliinn

Co.,

i%4.Pontic instrument for thousands of patients (Manuscript received July 11, i9i«j)

behavioral

Science,

Volume 12, 1967

Behavioral

Science,

Volume 12, 1967

Computers in Behavioral Science 2r,f)

j

RECENT DEVELOPMENTS IN THE PSYCHIATRIC CASE HISTORYEVENT SYSTEM1

by Bermce T. Eitlustrrt,Samuel H. Brooks, Rotro L. Motto, Arthur PlaU, antl Robert Cannichael

Reiss- Davis Child Study Center, Los Angeles

Orallyreposed in «*mented on a large scale in a study of the decision

q"*£" ° for conversionof case his-The EventSystem is a generalized:^"f0f rK.I and analysis. The de-tory data into computer acceptable fo" f»5 ■"JswStiwi to computer output are nowpre-

clinical and research endeavors, are made evident.C*J>

«„, Psychiatric Case History Event or format1 System (PsyCHES) is a generated the actualinformation-processing system «' *tonng P' «

()f data , from the point of tran-and analyzrng the comply body i nfor pr « g^mation contained ,n the c meal

<^rt « r «rrp

■

. entati()lls d

plyXtnf int iSS K refer to some research applications to date.

SST t^TRANSCRIPTS OF CUBICAL DATA

undergone considerable development, as it TheEvent Modelhas been implemented on a large scale in an

f t: The structure ofinvestigation of the decisron-makingproems by establishingin psychiatric* diagnosis.' In this project the an eve t > ()f datafactors affecting decrsron-makrng processes the genc "£' The definition

, it*

",>w

Anfi ind reuuires the use nmcant person in i«» ■"-,body of complex data, dim re. ." tual happenings, such as rll-of irrformatron processrng, statistical arm i.vuiu

.th H

"

the birthother analytical techniques which can be nesses, apporn menfc at

)mirren,espractically imputed t^^^ or outside validityputer processing of the dat, . A JW 1

b considered as ob ective, factuathe project, there cjre was J^^ Y^ hologMconversion of case his^Y phenomena such as fears,

fantasies,

reac-into computer-aeccptaDlc torm. P fft whiph (!annot be

In this paper we shall show the prcsc .^ h&yc

ST °f nISTm lodeLTlK po" outside validity. Included in the sec-We would Ike first to etc moist" * attitudes, opinions, rmpres-Kvcnt Model which provides the structure oru^g JP^^ subjectivc„r

> Presented in part at themeetings of the Amer- " (, ' ntal phenomena which psychratnsts

ican Statistical Association Sessum on DMa- JreleVant in describing the behavior

' by the U.S. Public Health Service, thority are examples of this kind ot

National Institute of Mental Health GrantsMil 11306-01 and Mil 11306-02.

254

Behavioral

Science,

Volume 12. 1967

Report DataEvent Name: Report**1 Mode and conditions by

which event informa-tion was reported tothe recordDiltf'* Date of reportDa*te Type* Source and estimatedaccuracy of date ofreport

Location* pjil{.(! r(,port oriKirmtedReporter* Origin reporterModifier: Context* Type of reportModifier: Circum- Elaboration of type ofstance reportModifier. Name Name of professional

reporter, if

any

Modifier: Location Elaboration of locationEvent DataInvent Name* Content categoryDate (Beginning)* Date of onset of' eventDate* Type* Source and estimated

accuracy of beginningdate

Date (Ending) Date of conclusion ofevent

Date Type Source and estimatedaccuracy ot endingdate

Location* ]>]aeP whorp (, v(,nt (1( ._curred

Reporter* Event reporterSubject* Subject of the event

mentModifiers* Uniquecharacteristics of

the event to be chosen,as appropriate, fromthe following list ofdescriptors:

Modifier: Attitude Modifier: NameModifier: Change Modifier: NatureModifier: Circum- Modifier: Nostance

Modifier: Concurrent Modifier: ReasonModifier: Context Modifier: ReferenceModifier: Degree Modifier: ResponseModifier: Duration Modifier: SequelaeModifier: Example Modifier: StatusModifier: Frequency Modifier: SupplementModifier: Location Modifier: Test findingIrr each field only u number, words, or

The model for the Kvent Statement con- symbols which are in a class implied by thatfains the following fields:

n;lme

iirc expected. It is not essential thatilll ,ields specified for an event be used atri

3 The starredfields are mandatory for all events. ' h 'The fields which are notstarred are optional; that Transcribing sentences into events,s = data are entered only when relevant, or „,appropriate. The decisions regarding the use of Procedure for the conversion of'nese optional fields are presented in Eiduson source material into a series of event*. i«a'

,d S,aff

(1960) ' "penned "d United in the Tntns^lLnBehavioral

Science,

Volume

12,

1967

cept can encompass all of the basic contentor kinds of information found in the bodyof the record. Thus the event becomes thebasic; unit for transcribing the total body ofinformation in the record.

The Event Statement: The Event State-ment contains certain basic elements ofdata which are common to all events: theevent name, which indicates the generaltype or category of the event, arrd the loca-tion, date, subject, object, and reporterassociated with the event. These data ele-ments are derived from the source materialby the rules and conventions of this systemand with the aid of lexicons, and form a setof fixed fields which constitute the codifiedportion of the event. The event generallycontains further specific or supplementarydata which are unique to it; this informationis organized as a series of "modifiers" to theevent, each consisting of a modifier name,which indicates the general type of categoryof the supplementary information, arrd amodifier entry, which contains the supple-mentary information itself. This set ofmodifiers forms a set of variable fields whichconstitute the uncodified portion of theevent, Thus, the Event Concept provides amorphological model for the structuring ofnarrative as well as normarrative data intoa set of computer-acceptable informationunits.

A special type of everrt is generated bythe occurrence of each individual reportcontained within the case history. This"report" event is analyzed irr the computerprocessing of the data, and pertinent infor-mation concerning the origin of the report(report type, author, location, and date) isappended to each event derived from thatParticular report. In this way, data con-cerning the origirr of the event become anintegral part of the event itself.

Studies have shown that the Event Con- Field Name Field Description

statement()l)i <>ct' Object of the eventstate-

Computers in BEHAvroitAL Science Computers in Behavioral Science256 2.57

Manual with Lexicons (Eiduson and staff, If a date cannot be determined from the

96 rHcTwever scrme general considerations material, the date of the report rs used and!!ith!,S- is characterized as "unknown." If an event

1 The event name is determined in each is concerned with information (such as a

instance by consideration of the nature personal characterrstrc) which rs unrelated

;

the event the context in which it occurs, to any particular tame period, he report

le ligut g of the source material, and by date is used and characterized « "currentuit uuigu 6 Supplementary information which is time-

rC^r l^iX^a into events, it is relevant (such I "When patient was two

essenS t conform to the general eon- years old") is noted within Ae event as a

vention of adhering to the event name used particular type of modifier ( date supple-

in the text or as closely as possible to actual ment").

;

worfs SdTn^Hychiatric records. However, 3. The location derived for each even ,upon r'asi n tbe psyc*hiatric or technical may be a specific type of Location such as

meli g o be wod'must also be take.r "home," "school," or "clime," or it may be

mtoccmsideraticrniri order to determine the a more general type of location such as

«*£. ton.She event category. For example, "general," "unknown" or "not Portnientn the enterrce, "We en.Lraged her to go depending upon the type oi event and the

back to the family agency," the word "en- details available in the record,

courage » wWeh coufd be the clue to the Supplementary information which amph-

e^TwouM be ambiguous. A word such as lies or specrfies the kjcatiori such as he = :

"^^^^r^\: Smr^^^iS^of ;

-£at (£S*^;p^ istead the context of the sentence helps in elements very often refer to relatives of the

;

h"ten X ion. This

,*an

be illustrated patient. In these cases the terms used must

!is fcibw " The wc.rd "anxious" found in the indicate relationship to the patient, regard-

rewles not always trigger off theEvent : less of the terminology of tbe.source ma-

ANXIETY- rather, the context of the sen- terral (for example, if the patient s mother

fenc^ mustbe taken into account* in deter- refers to her father during an interview

mMug the event name chosen. The sen- this person must be described_as maternal

te ice "He is anxious," may indeed lead to grandfather" rather than "father ).

io ;of the Event.ANXIETY; However, Any of these data dementsDjy««he sentence "Anxiety is one of his chief a set of two (such as "father and brother ).

vmZm'' would lead to the choice of 5. All other information specific or sup-

rSSoßlSs AND SYMPTOMS. plementary to the event is organued as a

9 A dite md a date type, or a set of series of discrete modifiers and arranged in

clates^ci IS, types, are derived for each logical order. Each modifier consrsts of a

by extracSr. of specific dates con- modifier name, wh.di indicates the general

air ed in the record or by analysis of time- type or category oi he supplementary m-

:^a,rphnises such as "when'patient was formation (for example "^u^^^two years old." The date information so stance," "examp e ), and a modifier entry,

de vel may indicate a specific date (for which contains the supplementary ,n orm -example January 1, 19(55, current), an tion itself. The modifier entries retain as

appZximate date (January,' 1965, approxi- closely as possible the original words andmate), a specific time period (January 1, phrases of the source material.

"SS beginning to February 1, 1965, end- «. Relationshipor dependence of eve- s >_ing) 'or an approximate time period (Jan- noted as a particular type of mod ha

nry 1 1965 approximate beginning to ("reference"). This relationship is taken

Sruar'v 1, 1965, approximate ending). into account rn data-retrieval procedures.

Behaviorar

Science,

Vorume 12, 1967

The abstraction procedure involves the Wh„„ +kfollowing steps, each of which is fullv de n 1gr°Upmg ot two or m,,re "de-

scribed in Eidu on and pr^ect s aff 966V P ?", T',tH Wlthin il *"te^ <* theirI. The statements ofX!

S

c f„ Is are' P ,re, .at,o» 8hiP f <> ««* other must be(.rganized into sets c>f discrete even P ?"' "' the tran««ription, the two

2. The category, and thus the event name Thol^r U> e^h other.is determined for each event ' rhe is

ac*

( ,,mplished by use3- The dates, location, reporter subject ' Modlffr: Reference,

and object associated with the even ,e' , I 'T^T" M a "mPlex °"e' a "ewdetermined. nt a'

C

T ahould be "Educed whenever any4. The supplementary data of the event «pXT*e^ "' °biect 'are organized as a set of discrete modifier event " °f the ori«inal

as^^^'^rit^pEes3 IEXT? : " FOl!:,Wmg **»*-*the source material. DepencW.Sw iZ^U^ ** — «*"at^cSar'C5 " ""'"^ ** two events:

*r). The category, and thus the modifier Th? first event has l« '*« subject: Terryname, is determined for each modifier entry. (Patient)

Event Name LEGAL ACTIONWhen this logical process has been com-

M,,difier:

Na""-e Detention 'pleted all data elements of the event have The second event has as its snhWtbeerr determined and are ready for record- object: parentsmg in standard form. vent

;

Name CONSULTATION. Usually every sentence is transcribed JfS,tr.: S£"„oe ?"^^' attorneyinto a single Event Statement. Therationale * Ve 'Uunderlying this general convention is that . Whe" a sentence has two ideas, but onethe English sentence characteristically con-

1S

doPendent on the other, both are trintarns oire independent piece of information <'nbed under one event. In order to deter-wrth other modifying or descriptive words mmc whi(;h idea h»« priority as the eventor phrases. Frequently, however, English the '"dependent part of the sentence takessentences are complex. They can contain Preference.more than one independent clause, as well Example: "Being tied to his parents heas dependent clauses or phrases which con- af,<'eP ts the values they place on education "vey data. Therefore, certain conventions Kvent Name V*UUFSapply ,n regard to determining the number Modifier: Nature Accepts "parents' valuesot events m a sentence (Eduson and pro- c on education ' '"ject staff I960). For example: sentences Mod,her: Reas"» Being tied to his parentsare generally parsed into as many inde- In general, the preference in transcribingpendent or discrete events as necessary to complex data according to the lC t Svafinclude the entire sentence. However, when tem is to parse the data into as sm*i l ,£'the sentence has two or more ideas, usually rate units as possible without' destrovintm two independent clauses, each may be- the meaning of the information recordedcome a separate Event Statement. Thus, it is possible to transcribe all recordExample: "He is tied to his parents and data sequentially sentence by 'sentenceaccepts the values they place on education." Thls sequencing, plus the use of the crossrefer(-nce modifier, bridges the isolation 'ofWName DEPENDENCY NEEDS discrete units demanded by the FveModifier: Nature Tied to his parents System. J-VCIlt

Event. Name VALUES

I?,",+

.„ A .Modifier: Nature Accepts parents' valuesrepresentation

Modifier: Reference iCi^e.,t Jl^^ T? "^"P^"-tcirion under the present format, examples

Behavioral

Science,

Volume

12,

1967

Computers in Behavioral Science Computers in Behavioral Science258 259... , i,u

.,.ti„n

Heiss-Davisof thc transcription of sentences from clinical j^^ Psy ehiatristnarrative are presented: Subject !>f '^i

Example 1: Sentence contaimng data Modirier: Nature Blunted

that could be objectively validated; "hard"11M<VPIMV AT ,,

data in its original arrd transcribed format Namc emc >TIONAL BE-Or'ieinal text- "The patient's maternal HAVIOItOrrginal T,cxi i

Date (Be Sinnins) Ju1y1,1%5aunt reported that in .Jury

iw<h,

tV,.o Tvnc Currenteloped with her boyfriend ,n a common- I^lypc HeisS -Davislaw marriage in North Dakota. Reporter Psychmtnst

Subject PatientS. Tomaeh, M-S.W. MolM',r: status bi generalIntake Interview 7-17-02 Modifi(,r . Nature ConstrictedReiss-Davis, Case lo Modifier: Nature Repressed

Transcription: Modifier : Reference Previous event

ously transcribed data manually. This task events TV.» fl uv.

*,

is handled by computer programming 1 II I flex,b,,,ty thu« Provided forIn the Everrt Lexicon each ev«i l's de Ttttb,lHhment f "<>u« hierarchies of

fined functionally. BecaJsethe trmscr nt on" T""T^ V " ildva» ta^ Eventsprocedures demll the c^t £j£n£ Kof dTe^"blT""^*. ° Umo-f original words whenever nossihle it ♦ / a? subsets> as deemed appro-was thought unnecessary o ve fo ' f *?* th? reHca ",h Pn,blwn w "tidy""omplete and inclusive definition Teac^ ""lb '"r^**"""- Pr»"<»> i« madeevent and delineation of Hs p^L ric *mJ tf^tZ" for thepsychological connotations As we lave m n ' , CTCnt n.am°

S

!Ui «muP» »r hi«"r-tioned previously, the tran c"ip io^ nro

"

*'h^ tor data r^r,fvtil purposes.ess is a process ofrestruc-turirrT he se Ue *c

"

WOVer ' ."' wde'' to develop uniformityor unit of information iS tr e T.hFTT"' '■ h" bGeU <lcsinible to «nician into stereotyped format Thus Iv* , 1 f ',ertjim pr,°ntieB for «»>"1-ge, the words mthiSVecc'rd * e,,t.^ "ne

ueVe"t . Pather thtt» another when

determine the choice of e^ent Ho" po^b,e .+fllte . lat,vcs exist. Here,

,*ertain

o<Tasional]y, direc-t word terns *ri fSi dis' "'f'",t,eH '.".

ll^e,

which have beentorts the psychiatric moUn^ 1 wes t" " t h" JTr"1 ""^T ? "^^ ron-ambiguous. In these instances thc Iv *b

tu.,t '.+havc. bwn capitalized upon. The

atric context must be te^nto "„S" ASThVT b'r ,ikp«on; and for these occasions, a fuXnal I""^"'definition of each event is nrovided

»,,H

fi, a : , ,!AL HISTORY, is one ofthe conditions delimiting ts use am f sc^me y ''^ the Seneral l^K""ent ofsynonyms and keywords re Hted D?f T?7 7T" °f bchttvior Whi("h *»;'eren(

*es

and similarities to o her c^ely a-" * S^d 1"bh'" Path°1<W' tend to belied events are also indicated. * f^i T b?hav"jr cve» ts." "» distin-

The lexicon draws its list of events from T ,T

fun,-t

!ons or *"»* >"n whichthe wide range of data wh «*h <*i 1T T^'i d,e,Hy' °r dysfu»»' ti«» exist, whichpatient case history, iT^lic7,"d h e h , " rbbl eVOnta-" Withi»

Jher

mformat.,,!

,S which J*1^" "b^^

curr^re T '*" *« «^I

interviews have permitted inclusion to

*,

FATivr ? \ andlesser extent, of he terminologv ther*, 1 t T ui

""b.sequent-ly. It has beenpeutic: interventions termino,ogy ()f thera

"

th"^ desrrable to optimize such intra-The Event Lexicon includes terminologv iTntifieT T^T Whe" ,thcy have bce»

descriptive of many .evels ofk^£fr °W ofconceptuahzatmn. This again has resulted Basing our events on ,.<■.,

,1

r" ,from the empirical orientation of the Event data m.v t l^ mca] TemrdSystem, in which adherence to the workmg "d" insZcL XV\T<vocabulary of the clinician is emphasized single ii^^fr^^*" "' T*The inclusion of class or generic categories SuSr C «suu f„ "" u? f ."" an°ther-along with those which are highly specific corporate wore s,l >

tendency to ,n--also demonstrates the lack of any impositior aZ2 I"";!^"!

OI

!ly Wh°^ theyof a priori categories in the formulation of miTiertSorin^ el"«* w^ul arrd

iiitLT, certain criteria. I<urther, close attention

,!

Details of this procedure will be found in at() the working vocabulary of the elinieiantorthcommg Manual of Data Preparation and has shown us that no single frame of referComputer Procedures. „.,„„

ai;

o a.; rl „ " 1 .

'

, lldmt ()I reter-ence distinguishes written data. While oper-

Report Data reportEvent Name

juiy 17, 1062 The Event LexiconDate Type Beginnins The Event Lexicon presently in use inL "<'a,"m wtl?&r the Psychiatric Case History Event SystemRep,°lt("' ri .t

1,

tattu" view ,mHistyH of some 250 events. On the basis o

sis; s^r » : T—h. m.s.w. empiri(.al <-f -f*^t;;child psychiatrrc tacihtres, these events

kZI N^me MAKKIAOK havc emer ged as categories for classrfyrngnlte (Beginnins) July *"*«. thc eontent of the narrative text, question-Date Type Approximate medi( .al form data contained rnDate (Uncling) j DSVchiatric clinical case records. The event*»ate/,oT State were selected primarily on the basis of the

Reporter Maternal Aunt frequency with which the kinds of intorma-Subject »^il>nt ti<m they convey were found in the caseObject -

n, . records As such, this lexicon of events

SI'S Xrcm..- &mw th ...yfnend etits the kinds of data most likely to

Modifier. Crcum re(,()rd ()t thoModifier: Location Statedorth Dakota

( , hild patient and his family.. . „ , *v, Th ioxj(.(m is open-ended. New events

Example 2: Sentence con anung s, H J^' fey (!lhm,anH isdata. This is presented to show that this aic^aoc demand a new lexiconkind of information rs transcribed m the *^J t ()f dinical datasame way as is more objective da a at*,vc wmd and f readily incor.

Original text: "Affect Runted «,^_ a^b IP^ |fpatient's emotional behavior in £ | h , lia „ omlrs so frequently in an insti-eral seemed constrrcted and repressed. uhoU .

g

Hkely t<)

j

Jones,

M.D. be desired, a new event is added to thePsychiatric Kxamination 7-1 -<i5 lexk.on ]f the frequency rs too minimal toReiss-Davis, Case 2!)

suggest a strong probability of future selec-Transcription: ' .

" "

this same clinical phenomenon wouldReportData' subsumed under the Event: SPEECH.SrN*!^ AKKECT _ b4vTvent errtries may also be introduced to

Date (Beginning) July l , l«» handle data unique to any singleiinstitutum.

Date Type Current Whenever a specific event like ECHO-

nresented at this time for comparison. . general Event: SPEECH. 1 ollowrng sue i

5 Report data follows the sameformat as mi'ax-(.hauge there is no need to update previ-

ample 1 , so it is not. presented there.

Date Type Current

Behavioral

Science,

Volume

12,

1967Behavioral

Science,

Volume 12, 1967

Computers in Behavioral ScienceComputers in Behavioral Science 261260

i u ...aß.aat.Hits This was a slow andating within the confines of certain gerrend search **ikU

conceptual schema, clinicians seem to utilize Wltha number of overlapping frames o refererrce^ hrs^ ta£We anticipate that our own stud.es n he he..* sta^^ rQ

_future will be helpful »i elucidating fore, *V»[ QT iece ()f informa-relationshipbetweentheoretical referent and stun turcica .^working implementation. fonnat _Manual of Transcription Procedures The

The generality of the Event Syster*r ha. elements (as defined by thebeen demonstrated ma number of feasibility »P

} dictated in the followingstudies involving different purposes and foUowmg

using different ki.rds of data. As a result, sequence.Mandatory

several institutions have expressed the desire (1) Eve„t. Name M^nda y

Kite the Psychiatric Case History Event (2)

Oat,

Mandatory

System for their own research, clinical, ant t . Optionaladministrative procedures. Toward this end , )ate Type (2nd) / Mandatorya Manual of Transcription F"«£^ *«£ (0) Wionnublished (Eiduson and staff, 1966). Ihrs (7) Reporter Optionalpub ,Sm provides the explicit procedures (8) Reporter (2nd)

governing tnu.scription to the pent of key- W SubjectPThihe manual, separate sections cover gg SK (2nd) Optional

each fcrrmat field used in translating narn, (13) Modihe, Nan» Mandatory

five material into the logical scheme ot trie k Modifier Name (2nd)) OptionalEvent System: event field, date held, loca- ModiHer Kntry (2nd)]

tion held, subject and object fields, modifierfields, report date fields. Each section is

divided into two parts. Thc first diseUbhebModifier Name (9th)\ Optional

the general rules of usage; the second v(30) M()difior Elltry (9th)J

establishes theJLinuts of entries held

ttud Provrdes exam^cs the -m * n, sequem,e. Data elementsusing each term lht c cm nal mRy t()

Event terms which scivo as th J c nste■ i abstraction requirements;for the content analysis of cluneal data arc depc drng up ed!llT S^tfe^^Srsynonyms of Data* elements which are brad,

Ind.ces to events and tne y J preceding list are dependent; ifkey words are nududed te a , d, L*k a date type must follow; if acation and use. Ah^ ugh he « o,^v ()MR, it must be preceded bysynonyms and keywo^ mt« th p

everrt is a manual process, wh]ch requires n0

being automated. , mlt(MU.cs in the modifier- in this case only the special modi-Samples of transcribed sentences in tne moditicr, in. j

indicate to themanual document the procedural conven- herntune <)ft'ons. modifier entry was not due to error.

CHAIN OF TRANSCRIPTION AND In addition to the data elements listed,

COMPUTER OUTPUT tw(j ( .ial terms are dictated to indicate to

Transcription by dictation the keypuncher that key data elements arelranscripuun »y follow The term Hew event .. . P"-

In the original development ot the Event U , to .

"

iudi( .ateB that aSystem, data presented in the clinical record '^^ . bei dictated; the code-were manually transc*ribed by trained re- new event

Optional

OptionalMandatory

Mandatory

Behavioral

Science,

Volume

12,

1967

vmt *md'Sc,'t.,s t£7 for the k^^^^ of dictated material.Tei.;^^ r teda neW m

CT

S6t Data 1S in a free-form ratheris be ng dictated. than a fixed-field

format,

There are noAll dictation is in full English words and restrictions, within the available 70 columnsnumerics alphabetic code* are not allowed on the placement of data or on the co 2be" Ihem

C

<,U,ty °f differc"tiatil^ *" «' data entries from column mUZeoewecn mem. (;ard t() ( ,o]umn j ()f th D , fProper sequence of the dictated data are punched

t

as standard keypunch ab"^elements is of critical importance, since the tions (event name, date type, locateidentity of each data element is generally reporter, subject, obe.it, modffier namf) asdetermined by is sequentialposition within standard keypunch forms (date), orTnathe event set

;

only event names and modifier ural English as dictated (mod her entrvnames are specially labelled as such in the The standard keypunch ib Sic> 2edictation process, as described above. concise mnemonic forms which have beenThe actual terms dictated for each data established by the keypunch operators themelement mus be valid forms for that partic- selves and which have proven So be pracSular clement. Dictated terms for event and efficient. Special keypunch codes hivenames, dates, date types, locations, re- been established to correct errors a thlyporters, subjects objects, and modifier occur in keypunching, thus eliminating thenames must be val,d forms as defined by a necessity of correcting and repCiglSlist of standard dictation forms and key- vidual Pards. Omissions or unintelligiWepunch abbreviations. Modifier entries are dictation are indicated by other ZSdictated as words, phrases, or sentences, and codesare restricted only by the requirements of As'described above, event names areZlelth " h WOrdS and

reaS

°n

"

P—dec! by the dictated phrase "new eter""

c

rp, R ".. . , , and modifier names are preceded by the codeThe reporting information which provides word «mod ." All other data elements areorigin information ,s dictated also by the identified only by theirsequentialposition [ntranscriber. However, this ,„ done only at the dictated event set, and by he use ofthe begmnmg of a repeat, Since a report valid dictation forms for that particulardatausually contains a number of events, the element. Event sets are preceded by thecomputer

1S

programmed to affix automati- special character "/" (slash) which is inter-caHy the reporting

,nformat,on

toeach event pretedby the computer program as an eventsubsequently. separator. Data fields, other than eventv _■ r j*

a.

.. j . . name, are preceded by the special characterKeypunching of dictated material «*"" /naTAr;aM „,,;„/" " ;P

tt,ai

V ' 7,(asterisk; which is interpreted by theThe dictated case material is then key- computer program as a field separator.Datapunched on IBM cards by keypunch opera- fields may contain double entries (for exam-

tors who are familiar with psychiatric pie, afirst and secondsubject); in these casesterminology and with the particular conven- thekeypunch abbreviations are separatedbytions of thePsyCHES System. Keypunching the connector "&" (ampersaird).is doneon presequenced card deckswhich areproduced by a utility computer program. Computer processingCase numbers and other control information The keypunched case history data decksare automatically punched in each card are then put into the computer for initialduring the keypunch operation.This control processing. The raw input data is acceptedinformation (columns 70 through 80) is analyzed by the computer program for con-analyzed during computer processing to formance to the system rules and conven-verify that each input data deck is complete tions, converted to magnetic tape formatand in proper sequence and to allow the and added to the case history master file!machine sorting of mishandled decks. The entries for each basic data field areCard columns 1 through 70 are available verified by comparison against system lexi-

Behavioral

Science,

Volume

12,

1967

Computers in Behavioral ScienceComputers in Behavioral Science 263262

cons which contain all valid entries for each analyzed by Indfield Invalid entries which may be due to masterhie accordingly corrected, edited, and

_v£__i^&£ ££r£2aiSserror messages are gene atee A sequent^ nd^aj g

mmmm mm=m.that event tor rexrencc

&

uded English language

ttrifi,.»ti,.„ »rr,-.i*»., »*■ cditio,. Tho or to »v"*J JJ*W£- KnM

occur either ... trariscrrpt.on or -n key- P^^' pnjßnun main tenance. Thepunching. „„„„+„ nroarams were originally written for the

Correction data is generated by annota- pnigrams were g y

tion of the listing or by ."^afcon IBM 7WO 70J4 P J {applicable. Only those specrhc elementao at tte ttb

California,data required to correct the master fikm Schoolot A edic y

MOdd W' ri 'mltly

event numbers and field «»d«for^y«- '^^^the entire data baseby the computer program. Ihe correction ai i jndata deck is identified by the case number of^^ -- hut y

and by the current version number of the computer a.ce,

case. This version number w^chsaut, pote tiaj .^ mbMllwntmatically incremented each time tne case is Linff uv Wlrt n>nort generation, andcorrected, must be verified as current by the pre c-g byjort, re ie ;,program, since correction procedures may are relativelyinvolve the addition or deletion of events,

time however, andand thus theresequencing of the event num- and

sSln^tTt3?^rr£ I S^iures Jarryparticular study.

Behavioral

Science,

Volume 12, 1967

CHART 1INPUT DATA

(COMPUTER-EXPANDED)EVEnSo. m' 1"

'

DATE: MARCH 22, 1905LOCATION NOT PERTINENT CURRENTREPORTER PSYCHOLOGISTSUBJECT PATIENTEVENT DEFENSE MECHANISMS

MOD-1,

NATURE USES INTELLECTUAL TYPE DEFENSES

MOD-2,

REASON IN HIS ATTEMPT TO MAINTAIN CONTROL

MOD-3,

STATUS NOT ADEQUATELY DEVELOPED

MOD-4,

SEQUELAE DEFENSE STRUCTURE RIGID AND BRITTLE

ORIGIN,

PSYCHOLOGICAL EVALUATION BY PSYCHOLOGIST AT REISS-DAYIS,IVIAlteon ii, 1905.

detail listings containing only the dataelements required for the particular study.A single computerrun may search themasterfile for relevant data on a series of compoundconditions, and produce as output a seriesof detail listings in various sort orders, sum-mary reports, and statistical analyses, asrequired by the researcher.

2. An important concept, in the retrievalsystem as planned is the capacity for arbi-trary classifications to be assigned to relateddata elements, either as sets or hierarchies.For example, a set of event names may begrouped for retrieval purposes, and classifiedas "identifying data." These events maythen be retrieved and processed either byreference to their individual event names orby their status as members of the "identify-ing data" class. Such classification may betemporary,as in a one-time test of a hypoth-esis, or it may be made permanent if theclassification appears to be of gerreral value.This procedure will be developed as war-ranted by requirements.

3. Ultimately it is hoped that a retrievallarrguage will be developed that will allowthe researcher to employ the retrieval sys-tem independently of programming person-nel. This language would allow the researcherto specify in simple terms the nature andconditions of the inquiry, the statisticalanalysis recjuired, and the types of reportsdesired. A System User's Manual will beproduced which will describe the .salientfeatures of the system and which will includespecific directions for its application as aresearch tool.

Behaviorai

Science,

Volume

12,

1967

Current system development is thereforedirected toward the design and implementa-tion of retrieval programs of increasingpower, generality, and simplicity of applica-tion. These retrieval programs are hereconsidered to include special report genera-tion procedures specific to the project ma-terial and aims, the generation of rawstatistics, and automatic linkage to utilitysort and statistical analysis programs.

The retrieval programs are beirrg plannedto ensure logical development of a modularsystem which will steadily increase in power,generality, and flexibility,while the needforspecial programming decreases. Ultimately,it is hoped that the retrieval system will befully automatic; arrd may be applied by theresearcher without, intervention by program-ming personnel. Specific* developments of theretrieval system will depend partially uponthe directions in which our basic; investiga-tion progresses and the complexity of thequestions being asked of the system; how-ever, some of the basic considerations are asfollows:

1. Master file data will be searched andretrieved on any combination of parameterswhich are definable by the system. Rawstatistics will be generated optionally. Thedata or statistics so retrieved will automati-cally be reformatted as required for input tosort, report generation, and statistical anal-ysis computer programs; linkage to theseutility programs will also be automatic.Detail, group, or summary reports may beproduced. Optiorral report formats will rangefrom entire case history listings to special

Computers in Behavioral Science Computers in Behavioral Science264 265

IMPLEMENTATION OF THE SYSTEM date, our interests have not demanded thatIMPLEMEJNiAii attention be given to incorporating a

Clinical "real-time" capability in the Event System.,

ICtll-tli"v, _,

-Althoughthe Event System was developed At preseut dosed record data, or data that

primarily as a research instrument, its im- haye been gathered arrd typed into theplementation has permitted a number of reCoTd> have provided the basic source in-

clinical applications. formation. However, in the future, studiesApplication to records collected by other wUI be undertakento evaluate the feasibility

facilities: One feasibility study attempted to of putting information intocomputer accept-assess the ability of the Psychiatric; Case ab)e formj as it js actively gathered. Simul-History Event System to handle clinical taneousiy) certain methods of automating

records gathered by five child psychiatric fhe entire input pr( ,cess are being exploredfacilities in the Los Angeles area according ()m .c at, tive ( ,ases arc routinely processed,to their customary procedures and for their jt js antk:ipated that a number of clinicalown purposes. No difficultywas encountered appi i(>ations will become common practice,in incorporating total data from records K()r exampiCj the clirrician would have avail-collected at other facilities into the Event able tf) him all or part of the prevrousSystem. Thus, the generalizability of the (.()ntacts wjth the patient for "instant re-

system for data generated at resources other yiew_» gU(,n review is frecmently demandedthan the one where it was developed, was pri()r t() subsequentpatient contact. For thisdemonstrated. purpose, it may be helpful to have the infor-

Application to a variety of source docu- matiou already obtained, updated, and listedments: In the course of transcribing a body chronologically. Air example of thrs rs pro-

of 50 records collected at ourown and other vided in Figure j .facilities, certain source documents which It ig

COlU

.eivable that such a listing would

were no'nroutine in the psychiatric; record be vaiuabie iu exposing voids or omissions

were exposed to the Event System. These jn inf()rmation obtained to date. Chrorro-included: logical listings can also point to srmultane-

(a) detailed medical and neurological ex- itjes or phase relationships m the temporalaminational findings occurrence of events in the patient's life, or

(b) quantitative psychological test scores jn hjs therapeutic behavior which warrant

from such tests as the MMPI and the further exploration.Rorschach When data are sorted according to the

(c) EEC tracings eventfield

first,

gaps anddiscrepancies in the(d) clinical chemistry laboratory readings data bank become exposed. If the clinician(e) nurses' and aides' notes, referrable to desires to elicit further data to increase

behavior duringhospitalization. reliability or recall by the patient, he can

(f) process notes of therapists rn both readjiy do so.irrdividual and group diagnostic and treat- The capac; ty to have available for "rn-

ment sessions stant review" a number of cases (or parts o

(g) administrative and statistical records. ( ,ases) ah„wing similar characteristics should

+ , , provide the clinician with the kind of dataThese documents presented event data wQuld be directly useful in enhancing

which supplemented those found more rou- clini( ,ai services. Instant or regular re-tinely in such reports as application blanks, rf Qwn patient work, and that of hisnotes, telephone calls, school reports, psy- (i()11 eßj regarding patients with similarchiatric; examinations, summarres of psycho- toms and background, should sharpenlogical test findings, conference notes, and de(.JHion( , and practic;es.interview summaries.

The Event System was found to present Researchan appropriate format for each oi these new inwstigations eraploy ing

"tSd clinical implementation: To the Psychiatric Case History Event System,

CHRONOLOGICAL LISTING SUBJECT: MOTHERCASE: 197ORIGIN: INTAKE INTERVIEW BY SOCIALWORKER AT REISS-DAVIS, APRIL 271965 '

St^ES'" »»!»:" Kffi^—DATE, 1943, APPROXIMATE MOD-1 NATURE TTlum

charlcSristicLITY S>* """ fStenede tiAKAUIIuuSIIC MOD-3, NATURE SHY

St!^lSSIMATK M()I) change returned to maternal

*„„,

GRANDMOTHER

MOD-2,

SUPPLEMENT AFTER SHORT PERIOD

MOD-3,

DURATION STAYED ONE YEAR

"t!!S^SMATK mo,) - 1 ' CHANGE "" ANOTHER BREAK FROM„AI FAMILY

aVIOD-2,

LOCATION CAME TO CALIFORNIA (STATE,

MOD-3,

SUPPLEMENT AT AGE 27IjIVKmNIA iAU^

"t:Sp^SIMATE MOIM' """ CALIFORNIA (STATE)

DATE, 1947, APPROXIMATE

MOD-1,

NATURE UNHAPPYBEGINNING TO APRIL MOD-2 STATUS CHROmPAT I v27, 1905, CURRENT CHRONICALLYE"SMARITAL RELA

"

M°°-3 ' SUPPLEMENT FROM THE OUTSET

Fit;.

1

wc became aware that having a generalized tional data-collection practices? Fifty recinformation system essentially embodies a ords, ten of which were gathered in ell,fnew research strategy The new strategy five clinics, were transcribed) te ronjputerT hWa 3bda!TT

I IH.Ptable f°rm "*"* thG 'CSaSK. having a total data pool available, History Event System, so that comparative2. havm«.the computer'scapacity for easy studies of their structure and corrten ctilland efficient accessibility to all stored data, be undertaken

Co"nceTl;1 -TH""*'. iikr

th° ,Kve,l|

lteSU,tH

P°inted to "^^ diff"rru . . , total "umbers of events contained. HoweverThe new strategy has been applied in a despite the differences in the amount erf to * 1number of studies, of which only brief data gathered at the five clinics the 1£rsreference >s noted here

The,,*

details, how- distributed the information theyCTec'tec rver, reveal the krnds of data Election and essentially similar proportions amTg■, iTOmPUter baSGd ?tory °ategorieS (mcdi-] hiHtory.^iyapproach permits hlßt nt .„ yComparative studies of information con- usually covered in anamnesis tail }

tent in records collected at different facili- When the total ..*-*»»

,

<->,„ .ties: (Eiduson, Johnson, and Rottenberg, search fo ?d ta unttermte " W°r°1965) To what extent do clinical record da£ allvary as a function of differences ,n n.st,tu- invariant, Lowering the cut-off pTnt to 49

7 These capabilities are described in detail in t'^'?

CS

'nt'rocluccd 2 more invariant events.Eiduson, Brooks, Motto, Platz, and Carmichael w»en the sample is reduced to an Nof 40(in press), where comparison with the more con- that is, 80%. of the total,only 18 events wereveil tiona research approaches is made Tv>,it;.,aal.. a

,",

, vvi.iiahi sismauc routinely present, Further, comparative

Behavioral

Science,

Volume 12, 1967Behavioral

Science,

Volume

12,

1967

Computers in Behavioral Science Computers in Behavioral Science 267266

M

!

!l

studies of data suggested that at best thereis only moderate agreement amongelrnrcs on 0 life'" n"* Behavioral and bio-the specific data that are likely tobe relevant and^ '*7 > ° human infantfor the diagnostic; evaluation of the patient: <*cm»ca.l stud c «t a. c

40% of the total information wan found n. won. und^Ukcr^ «*records of all clinics; an addrtional 8^ hfcjha^J tate ()f arousa, a.»dcommon to three chines, wfcfe l7 W =ters

were unrc^e events, that is tcnind spc cm analyses, were» -^m*. '"::! cr„fS,: i

1: * « c,"i .*■- «*«__.

»"* . *-*■»an mcrdence of 8-1.) A* ot Uaita rn uie 1 v data-collection methods in-

clinical rec;ord samples as redundant, lypes period Ihe date

"^ bof redundancies and areas of information in

which they appeared were also .^plorcd^ tional hartu,^ f^Comparative studies of learning problems 1.1 ordtr te comp

convertedin latency age boys: (Eiduson Johnscm and -non sou c<^, U.

.^^Rottenberg, 1966) Recent stud.es ot ch.ldicn to the eve■*£»> This permit tedwith learning problems rdentrhed a numte tion »<» rolationßhipß exist-of learning 1 g"ig ihavioril parameters and amongand P^hodynarmc totu«*. f^ biochemical dimensions independently asdromes were established _by mve. j relationships between certain bchav-working m varrous Lai and biochemical phenomena,raised the question of whether the varum

different data sources could

z^z^!sr-£^Tl ;p ;s; '.rr** -i —population samples, or to differences ... the of schooJLulncal information on wh.ch studres were to7ftcilitate

'^"information in the records formed the an -^^^t^S^te^data base arrd was transcribedrnto^veri sor dre tmd te u^ie .^^variables and related modifiers. This («- l '^^^^e^^ions of classroom inci-verted the total body of record data rnto .1 stresses

"^ . collectedf(,m in which it could be readil^am^ dente -^^^* publK*Lhool sys-ulated, sorted, summarized, and ,nb^ l""L^dcr the direction of Dr. Nadine

(irr Predion) of the School ofEtr^:Sventr,m * ***\Sm relo ds were searched and re- utlliZed as the basic* data-collection method

Sr^thederivd data summarized in A lexi(,m of school events pertinent to hrs! numbed of wrtys* grade assessment arrd to the rrrstruct.onal' Presentations of the typical learning syn- sram, is being constructed. It rs antici-

dromes in each setting were displayed and , d that this lexicon WIH eventually be

.■ompared with the clinical pictures obtained u,d t() the longitudi.ral scho.,l experi-

when total data were analyzed as a single ()f ( .hUdren individually and of childrensample. Results showed the degree and ways

.^ Jn addition, it is expected thatin which the syndromes shift as a function ot ;ri( .al stud/ of teacher-pupil interven-fhe number of variables introduced into the wUI

ft

,cxi ,on of strategies

data pool from which the total picture is employc(l by teachers in thedrawn. , classroom situation.

Biochemical and behavioral study of an classrooi

Behavioral

Science,

Volume 12, 1967

Studies of clinical decision-making in REFERENCESdiagnosis: The purposeof this research is to Eiduson, Bernice T., Brooks S II & Mot,,, Helucidate certain aspects of the derision- A generalized' infortuL protS'^making process in psychiatric; diagnosis. We lem - Behav.

Sci.,

22, i960, 133-142propose to detail the steps or the mechanisms Kidus ""- Bernice T., Brooks, S. 11.,Motto, H Lin the process of diagnostic assessment; and w",A- & c »rmichael, '<■ New strategy"to understand the role that specific variables " PsyeKS" ll^eluS-itor kinds of information play in determining K. Laska, & N. S. Klein (Eds.) The "se ofor effecting diagnostic decisions. electronic devices in psychiatry New York'-The first phase of this study undertook Fidus.mT a S^"tton- i "/ress-c;omparativestudies of the structural charac £,vh , E!duso"> Ber""'e T. Certain be-feristics of records. It also stud^X "?^T^rTs^Zence of certain initial patient variables such Research in Child Development, New Yo'rkas age, sex, number of siblings, on these

,*"

, ml\Structural characteristics l*Hiu.soi. Bernice T Johnson, Trinidad C. M„ &

A further series of studies will investigate nS^' .1^,,,! 'Zl.lZ"^-three aspects of the decision-making process- Dams Chmc Bulletin, 2(2), 1965 59-68(1) the identification of the variables in pa-K,<llu"" ,

'^or»ice T. Johnson, Trinidad C. M„ &tient date that are discriminating for diag-nosis; (2) the sequential analysis of the Amer- J - Orthopsychiat., 26,1966 829-839 "decision-making process; and (3) studies of

Kl(1"so"' Bernice T., &

L.ibit/.,

Iris. CertainI,;*'"■ » " "**° "■ *«»»*» =p.l:l":r\S7C'zi,ris;:,£':

** g - psychiatric Association Meelinas, San Fran-It is anticipated that these data will pro P - c,sco' April > 1966> 222-223.

vide answers to the basic question- WhatiM

~'

Ber'lifif T - * p">'^ *<»«'■ Psychiatric

""—"■j f *.»*",im,i„g „v<:„: h;;: '^ff,^*^^,,*groups of events obtained at specific points ,)av,s Child St,ld.v

Center,

April 1966in the diagnostic study by specific clinicians Uimbev\' Nfdi»e; Studies of stress in the primaryi*«v« . s,rs ()re,li(,iv; u„ifi.,„„,. T sx-awasar.summary diagnostic decisions, such as diag- '"">«. Berkeley, in preparation,nosis, prognosis, and recommendations

"

nu

■

.(Manuscript received November 28, 1966)

C+J)

Every act that carries a definite damage to any other personbelongs to the sphere of law, and every act that can be su- po edhtwhat^Sr * lage' t0 that °f m°mlity ; andIf strictly .pressed, [this demarcation] excludes individuality fromevery act of life that has an important social bearing The demarca ion between individuality and society, contrived in defence"of the former, has pretty nearly annihilated it

Bernard Bosanquet

Behavioral

Science,

Volume 12, 1967

Computers in Behavioral Science

259

COMPUTER PROGRAM ABSTRACTS

Multivariate Statistical Programs, Dean J.Clyde, Thearbitrary

Eliot M.

Cramer,

Richard J.

Shenn,

Lniversity P^'^^te'Sray. The probability of correctof Miami. (CPA 242) transmission may range; from .5 (complete noise)

A series of programs written in FORTRAN I\ t() unity (perfect transmi

SS

ion) in .oi

for an IBM 7040 is available on a tapeby writing

Q5

ldentincation numbers aie assigned to eacnto the

'

Hiometiic Laboratory, Univeisity of noi instance to indicate the or.gmal pattern,

Miami P. O. Box 9175, Coral

Gables,

Flor.da Ae level> and the particular noisy instance.

33124 Printing of the patterns in black and white is

c ... , accomplished by overprinting an asterisk, a zero,

OMNITAB,

a Computer Program for Statistical f h black cell .Id Numerical Analysis, Joseph Hlsenrath, and th .ktter O yGuy G. Z^fer, Car/a G.

J/ewina,

PhilipJ■ J"*im an IBM 1620 (60 K) inFORTRAN IIWalsh, and Robert J. Uerbold, National Bureau jut subroutine m the lbrary

o/ Standard* //and&oofc ."* ££" subroutines. SCOPE 4 WRT was written for theL'. S. Government Printing Office, 19bb WUp. .^ symbolk , machine language, and

#3.00,

(CPA 243) requires a 1401 with space suppress feature

OMNITAB has gone somewhat further than Availabiiity : Copies of the listings ot bom

mos comprehensive statistical programs, by ograms, with a write-up of each may'be

toting an entire group of analyses under the btained from the T. C. U* Computer Cente ,3 of a singte statement. For instance: Texas Christian University, Fort \\orthlexasTOIYFIT COL ++, WEIGHTS IN ++, X 76129 . A listing and write-up of the randomIN ++ USE N DEGREE will cause the pro- number subroutine is also available,

gram to fit a polynomial of degree n to store

deviations of the computed values from the References:observed, to compute standard deviations ot the Facsimile-generated analogues lor

coefficients,

and so forth. Some of the programs C"k ;ilßtrumeiltttl forms displays- In J. W.

make extensive use of plotting to present visual k & j. „. laylor (I^-. «_summaries of the results. tXn^n^'c,Na^^vAnr.ns 6D: A Simple System for Producing ()f Sciences, National Research Council

three sets is-uolJ) ot the persons. Ihe nature of the solution, however,will to varying degrees allow us to make in-

/!> if p-> c for j ferences about both order and linearityinrespect\O, otherwise to thescUhree facets, whether the data be whollyquantitative,wholly qualitative, or a mixture ofthe binary matrix denned by the above func- quantities and qualities. All of this is a functiontion is called the attribute matrix E (of order * of constraining interset relationships to satisfy

tic XN, where ra, = the total number of cate- the nonmetric. inequality.gories over all items and Ar = the number of A MSA-I I solution will yield a set of distancespersons), and constitutes the basic data matrix which are partitionable into two classes corre-for both qualitative and quantitative data. This spending to the two partitions of E, such thatrectangular matrix can be viewed as a partial all distances smaller than the largest distanceadjacency matrix of a graph, whose row captions f°r which epjc = 1 will constitute one class andrepresent categories as one set of entities and the remaining distances the other class. Thewhose column captions represent persons as partitioning of D, the distance matrix, intoanother set of entities. Consider any two cate- these two classes based upon p„ r, hereafter re-tries of Cj, say b and c(b*c; b, crCiotj

■

fared to as the radius of inclusion, that is, the

i,

jr.J), and any member of the set P, say p. largest distance for which

e„

Jr =1, permits usA partial order can be defined consisting of to determine the value for the coefficient ofall those pairs for which e t>JC =1, as constituting reproducibility along lines similar to that ofone subset, and all those pairs for which a standard scalogram analysis (Guttman, 1944).epib =0, as the other subset. Thus, the two All points lying within tiie radius of inclusionpartitions of the pth column of E consist of all (most generally defining a hypersphere) forthose categories into which p falls, on the one which

e„

ic = 0 plus all points lying outsidehand, and all those categories into which p does

P,*<-

fol' which

e„

JC = 1 are considered errors innot fall, on the other. If

e„

jc = 1, we can say the calculation of ft = 1 - (errors/2raA),'thethat pis in relation to ceCj, otherwise not, coefficient of reprodueibilitv. To the degree 'thatand thisrelation is symmetric, that is, epjc = ejc „ . <t> >0, R will be less than unity. R serves nofiy using the foregoing notions it is possible to functional purpose, however, in the MaSA-IIdefine a concept of distance and dimensionality program, but does provide an interesting de-for the special case of a partial graph (see Gutt- scriptive measure of how well E can be re-man, 1964, for the treatment of these concepts produced.in relation to square, symmetric matrices). In summary, MSA-I I analyzes the irre-Thc specification equation for the multidi- ducible data matrix E (no derived measures ofVARGUS 6D: A Simple System for Producing ()f Sciences National Iteseare ounci Thp HJjecifi( .ation equation f()r the multidi. ducible data matrix E (no derived measures'of

"Noisy" Patterns (for IBM 1620 and IBMin research. New mensional analysis of E can be written along association based on E are employed) based

1401), 1 Selby 11. Evans, AA. J. "f""' (,reeuv"rk mSw-HUI^- e follo"nK lmcs m terms of «mall<*t space upon a definitional system of a binary relationand Malcolm D. Arnault, lexas Christian *"., M ' The role of redundancy in the theory (Guttman, in press; Lingoes, 1967; between members of two sets (categoriesUniversity. (CPA 244) Kapl

discrimination of visual forms. J. exp. m press a). For all pairs of relations between and persons), within the context of smallestDescription: Some kinds of research in pattern paychol., 1957, 53, 3-10.

neiception (such as Rappaport, 1957;

Crook,

957

Green,

1963) call for the generationof sets18M.7090 Program for Guttman-Lingoes

of patterns into which some controlled amount o MuUidimensional Scalogram Ana ysrs-11,**noise" has been introduced. Ihe VARGI S 61) u The Umverslty „f Michigan.

sV stem provides a means of introducing randomerror- into a 48 X 48 black and white array, by . The general probten of multi-transmitting ea<*h elementof the array with some anal is is concerned with the threepredetermined fixed probability of changa g he basic of QI objects (P) enableselement to its opposite state. Both the initial .^ and cateKones or rubrics (C

;

),

array and the noisy arrays are stored on caidb subß(!ril ,t on C implying that the categories

which can be printed in black and white, on| anfa tQ

&

particular item. Given these sets,

IHM 1401 with a second program, SCOI

\,

4 ])roblem for analysis is that, ot mapping I

WRT' ,

"

,uv i This research in nonmetric methods is sup-■ This research was supported .n part > a from the National

Grant PS 6562 from the Texas Chnsuan l^i- po ted P .()n 92<J)

versity Research Foundation, and by the lexas been*(m loave at The University of

versiiy iic»caiv.i»

* v-

-Christian University Computer Center.

2

Director,

T.C.U. Computer Center. California, Berkeley.

268

Behavioral

Science,

Vorume

12,

1967

, ...,.-.

-"

—

& * "

"""-■ v'lil'iva^t-aay uracilthe following lines in terms of smallest space upon a definitional system of a binary relationtheory (Guttman, in press; Lingoes, 1967; between members of two sets (categoriesin press a). For all pairs of relations between and persons), within the context of smallestmembers of the category set and members of space theory, which is totally free of thethe person set, satisfy the following inequality: usual assumptions of multivariate analysiswhenever (p, b) < {q,c) (b,cr C} ;j p J;p,qe P), Nevertheless, as a product of the nonmetricthen the Euclidean distance d„jc £ d„jh is the restrictions imposed by satisfying a set of in-smallest possible space, m, for which the co- equalities, a metric solution becomes possibleefficient of monotonicity (normalized phi) is in a joint Euclidean space of minimum dimena minimum. We are defining binary relations sionality. It would thus appear that MSA-IIin terms of a distance function such that all is excellently suited for the multidimensionalpoints in one set (categories) which are related analysis of a wide variety of data heretoforeto points in the other set (persons) will have either unanalyzable, or analyzable but basedsmaller distances in the joint space of persons upon unwarranted assumptions. The user ofand categories than all [joints (one from each this procedure should become familiar withset) which are not in relation. Nothing in this the basic; literature of smallest space theory-statement of the specification is implied about (which, in addition to the foregoing referencesorder within categories, within items, or within should include: Shepard, 1962a, b; Kruskal'persons. Nor are any assumptions required 1964a,b; and the paper describing'this procedure'regarding the marginal distributions of E. Nor in moredetail, Lingoes in press b)are we imposing any constraints upon linearity MSA-II differs from MS 4-1 outer-point

Behavioral

Science,

Volume

12,

1967

Computers in Behavioral Science Computers in Behavioral Science 271270l ■

t\

;„« sciences. Proc. International Symposium:multidimensional scalogram anal>sis (Lingoes,

Malhematical and Computational Methods1966a; in press b), in terms ot rationale, tne Sciences lnternat i onal Computa-algorithm employed, computational speed, ca-

( .(m Celltrej uome , in press, (a)

parity, simplicity of contour boundaries, and in Lin(?oeSi j.rj. The multivariate analysis of quah-generality but from examples run to date, the ta(ive data Proc . Conference on Clusterresults are remarkably similar both in terms of Analysis of Multivariate Data, in press, (b)

dimensionality arrd configuration (for an ex- Shepard, 11. N. The analysis of proximities:clunensionai y a k Multidimensional scaling with an m.km.wnample see Lingoes n P«^>- fun(, ti<m

_._

L R^hometnka, 1902,Capacity: (n„ A)„,„x S 3U, n

r

+ _ ,125-140. (a)

m S 10. _ Shepard' H. N. The analysis of proximities:Running time: Approximately the same as -i

M'uUitlimensi(mili scaling with an unknown \SSAR-I (Lingoes, 1966b). distance function -11. Psychome'rika, 1902,

Availability: Copies of the FOR IRAN 11 27, 219-245. (b)listings plus the write-ups (in the form of com-ment cards) can be obtained by writing toProfessor James C. Lingoes, Computing

Center,

An tBM 7090 FORTRAN IV Program to test

The University of .Michigan, Ann Arbor, Michi- linearity of regressions, Lennart Sjoberg and

iran For those desiring source decks and sample Lars Bergman, Psychological Laboratories, bm-

data, please send a new (or in good condition) versity of

Stockholm,

Sweden. (CPA 246)

IBM-729, Mod. II magnetic tape, upon which _roblem: Linear regressions between van-

willbe written the BCD card images (14 words/ The robem. g

record at 556 BPI), documented for conversion able au^ pre eq

to other systems. Decks of cards cannot, un- cmremn u

fortunately, be sent on request unless the cost of of lineariu, For this reason, itmaterials, labor, and postage is paid by trie

(.onsidered deßirable to have a separaterequestor. program available to make the check on regres-

sions The present programcomputes the correla-References: " .

q ea(,h regrcHsionB surface and alsoGuttman, L. A basis for scaling qualitative data. /(, frf variance aPCoUnted for in addition

Amer. social. Rev., 1944, 9, 13J-l.>u. reeression model. Scatter plots may

A Suchman, P. F. Lazarsfeld, Shirley A. correlations are also computed.Star &J A Clausen (Eds.) Measurement Output: The output is the following:

and prediction. Princeton, N. J.: Princeton * A w.atter plot (25 X 25, each cell blank orUniversity Press, 1950, Pp. 312-361. with its number of observations) for each pair ot

Guttman, L. A definition of dimensionality and varial)les that meets at least one of the threedistance for graphs. Mimeographed report, (iritpria mentjoned below. Each plot is accom--1964. . l . , nanied bv the correlation

coefficient,

the correla-

Gut.man,

L. A general nonmetric technique tor >» g l q{finding the smallest Euclidean space for a tion

ratios,

ini /

o

,6

Oration of point. Psychometrika, in freedom of these /" valuesconfiguration ot po.m .

./

:V matrix giving the inteicorrelations of allpress. . . , B

Kruskal J B Multidimensional scaling: A nil- variables.merical method. Psychomelrika, 1964, 29, 3 A matrixshowing the proportionof variance

1-27. (a) . accounted for by correlation ratios in addition to

Krushal, J. B. Multidimensional scaling by op- th(> linea). ,.ogl.ession for each pair of variables.timissing goodness of fit to a n.mmetnc .V matrix containing all correlation ratios. !hypothesis. Psychomelrika, 1964, ii, n*>

Scatter plot criteria: Each pair of variables is129. (b) -aVipekod accordinn to three criteria in the follow-

Liiiw>es JC An IBM-7090 programtor Guttman- checked accoiamg

Lingoes multidimensional scalogram analy- mg order: spiffedsis - I Behav Sci., 1966, 11, 76-78. (a) 1 . If one of the variables in the pai is spcciired

I iniroes" J C An IBM-7090 program for ;n advance by the user, a plot is performed.' GuUman-Lingoes smallest, space analysis 2 . If the pair is specified as interesting, a plot

HI. Behav.

Sci.,

1966, 11, 322. (b) is pelformed.Lingoes J. C. An IBM-7090 program for Guttman- 3 Thp twQ /(* vaiucs are tested for significance

Lingoes smallest space analysis HIV. Be-a(, (,or(ling to a specified level of significance. A

hav.

Sci.,

1967, 12, 74-75 performed if one F value in the pair isLiuuoes JC.

liecent,

computationaladvances in P

IOL

'h ■",* nonmetric methodology for the behavioral significant.

The program is designed for a variety ofrepeated-measuresand standard analysis of Vari-ance problems, handling one to five variables, ofwhich as many as two may be repeated-measuresvariables. All the required error terms, as well asall main effects and interactions, aie computed(cf. Winer, 1962). Punching data is simplified bygrouping all scores in a cell together and bygrouping all scores (lepeated-measures) for asubject, together, paralleling the usual arrange-ment, of recording data. Airy combination of four-transformations (power, log, reciprocal, and mul-tiplicative) of the raw scores is allowed and theresulting scores may be printed out.

Output provides: (a) a complete Stable con-sisting of sums of squares, degrees of freedom,mean squares, Fx, and identificationof error termused for each F obtained; and (b) all main effectand all interaction tables of means.

Variables and levels of variablesmay be namedfor output, labeling. Variableformat for input andoutput of scores is permitted. Several analysesmay be performed in one run on the computer.

Limitationson the numberof levels of variables(A, B, C, D, E) are: A S 8, B S 6, and C s 6;

1 This program was supported by Army MedicalKeseareh and Development Command ContractDA-49-193-MD-2831, National Instituteof MentalHealth Grant No. 0831502, National ScienceFoundation Grant No. GB 3404, and the Instituteof Behavioral Science of the University of Colo-rado. The program was written while the first,author was supported by a National ScienceFoundation Cooperative Fellowship.

Instead of using the three criteria one may Dg 4, and E< 4. The numberof data nointsrequest that al pairs or no pairs be plotted. (defined as the numberof & 2n "eat dSInput: Input is from punched cards. Missing times the number of scores per S) is limited todata ,s a lowed and ,s indicated by a negative 400 for the nonrepeated and 'one . ;pea edTeas£.H«, TT'i'T' , , i "Ul'

eS

Va 'iable <lesi«

nS

- Tht- is "» -stri bonT, rLimnations: Ihe total number of subjects the two repeated-measures variables designmust no exceed 370 and the number of variables , Computer: IBM 7044 with 32K core storageOU (or, it appropriate source program statements Language- FORTRAN IV '

musl't^t'ecJlsS"'' '"^ i *Unnin* Time: approximately one minute formust not excec d 18,500) loadmg) and so forth wjth executj ta]d fRunmng time: A number of data matriceshave 2to 9 secondsper analysis for those performedtobeen run with no plottingand running timeshave date. (The longer time was for an 8 X 4 x 3rarely exceeded 5 minutes Maximum data two repeated-measures variables design with 25matrices have not, been tried, however. & per cell, a total of 2400 scores )Availably:A descriptionand source program Availability:a write-up and listingare availablehstmg an, available free o charge from Lennart from Computer Program Librarian InstSe c,fS oberg Psycholog,c.al Laboratories, University Behavioral Science, University , f Coloradof Stockholm, Stockholm Va, Sweden. Boulder.Reference:Repeated-Measures Analysis ofVariance,' David

11. Dodd and Robert A. Kinsman, Institute ofBehavioral Science, University of Colorado,Boulder. (CPA 247)

Winer, B. J. Statistical principles in experimentaldesign. New York: McGraw-Hill, 1962.

Program to Compute Normalized RegressionCoefficients via Principal Components, Y.Uaitovsky, Israel Institute of Technology, Tech-nion City, Israel. (CPA 248)The program computes and prints out in itsfirst stage the; raw and centered cross-product

matrices, and the correlation matrix. Next, asubset of any size of the correlation matrix isfactor analyzed and the eigenvalues, their per-centage of the total variance, the normalizedeigenvectors, and the factor loadings are com-puted and printed out-

It has proved useful in many situations toregress a dependent variable on the ortho-gonalized linear combinations (factor scores) ofthe set of independent variables. Multicolline-anty in the latter set is a case in point. Theadvantage of this procedure is that one mayselect as many factors as he thinks will representthe correct dimensions of the set of the originalindependent variables, (cf. Kendall, 1957, ,,71-74). The final stageof the programis designedto do so; namely, the normalized partial regres-sion coefficients of a dependent variable areregressed on a subset of the factor scores com-puted at the factor analysis stage. This regressionmay be stepwise. In each step the programcomputes and prints out the factor scores, partialregression coefficients, /*' statistics, "Student" tstatistics, and the regression coefficients trans-formed back into the ordinary least-squaresnormalized regression coefficients.

Contrary to otherprograms such as BMD, thepartial regression coefficients are not computed

Behavioral

Science,

Volume

12,

1967Behavioral

Science,

Volume 12, 1967

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Computers in Behavioral Science272

i

by the actualregression of the dependentvariableon the factor scores, but a short cut is availablewhich increases computational accuracy andreduces computer time. For the mathematics seeBeaton (1964), Haitovsky (1966), and the user'sinstruction.

A brief description has been deposited asDocument No. 9348 with the ADI AuxiliaryPublications Project, Photo Duplication

Service,

Library of Congress, Washington 25, D. C, wherea copy may be obtained by citing the documentnumber and by remitting

$1.25

for photoprintsor

$1.25

for 35 mm microfilm. Advancepayment

is required. Make checksor money orders payableto:

Chief,

Photoduplication Service, Library of

References:

Beaton, A. E. The use of special matrix operatorsin statistical calculus. Research Bulletin.Educational Testing

Service,

Princeton,N. J. 1964. . .

Haitovsky, V. A note on regression on principalcomponents. Amer.

Statistician,

1966,20,28-29. . , .

Kendall, M.G. .4 course in multivariate analysis.New York:

Hafner,

1957.

0-9

The materialistic theory has all the completeness of the thought

of the middle ages, which had a complete answer to everything,be it in heaven or hell or in nature. There is a trrmness about it,

with its instantaneous present, its vanished past, its non-existentfuture, and its inert matter. This trimness is very medieval and

ill accords with brute facts.Alfred North Whitehead

Behavioral

Science,

Volume

12,

1967

Congress.