Fabian Adelt – Johannes Weyer - ea-aw.de · PDF filetechnische universität dortmund...

technische universität dortmund

Department of Economics and Social Sciences Technology Studies Group

Fabian Adelt – Johannes Weyer SKIN 3 Workshop, Budapest, May 2014

Adelt– Weyer | 2014



Content

1. The issue 2. Basic principles 3. Purpose and concept of SimCo 4. Simulation framework in detail 5. Results from previous work (SUMO-S)

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The issue: Innovation policy

Interventions into complex socio-technical systems Goals: stabilization: avoid breakdown, improve performance change an existing system

Problem: where / how / when to intervene?

Innovation policy needs knowledge about the functioning of complex socio-technical systems effects of interventions types of actors population

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Design principles

Model of sociological explanation (Hartmut Esser) James Coleman‘s „bathtube“

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Design principles


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logic of situation

logic of selection

logic of aggregation




Design principles


Two levels (and interdependencies between them) system dynamics (macro level)

emergent phenomenon actors‘ choices (micro level)

decision making, based on subjective expectations (range of view, „bounded rationality“)

different types of actors: rational egoist, norm follower, emotional man etc.

Impact of innovation policy and interventions on macro behaviour?

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Agent Based Modeling and Simulation (ABMS)

Agent State Transitions

Rules Actions Behaviour

Environment Information Structure

Time

Quelle: Van Dam et al. (2013:58)

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Agent Based Modeling and Simulation (ABMS)

Agent State Transitions

Rules Actions Behaviour

Environment Information Structure

Time

Quelle: Van Dam et al. (2013:58)

A micro-founded ABMS can implement a sociological explanation!

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general model of • networked infrastructure system

• energy, transportation, information • nodes, edges, agents,

technologies

• micro-macro-interaction • dynamic behaviour in time • innovation policy / governance

• instruments of intervention • management, control, regulation ...

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Agents‘ tasks

move in the network pursue individually

rated goals visit (selected) nodes use resources and

technologies stay within limits

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Decision making based on sociological theory of action

subjective expected utility actions: (individually) rated goals: probabilities: indicate how far action i serves goal j depend on state of the network and the actor’s position possibly influenced by interventions

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Choose the action that fits your goals best!




Type A 10 3 1 5

reach green nodes

behave eco-friendly

act money-saving

move fast

SEU

direct path

1 1 0 0 13

via gray node

0.5 0 1 1 11

Decision making based on sociological theory of action Example

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Rated goals

Act

ions

Probabilities?




Type A 10 3 1 5

reach green nodes

behave eco-friendly

act money-saving

move fast

SEU

direct path

1 1 0 0 13

via gray node

0.5 0 1 1 11


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Rated goals

Act

ions




Type B 8 3 5 3

reach green nodes

behave eco-friendly

act money-saving

move fast

SEU

direct path

1 1 0 0 11

via gray node

0.5 0 1 1 12


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Rated goals

Act

ions




Type B 8 3 5 3

reach green nodes

behave eco-friendly

act money-saving

move fast

SEU

direct path

1 1 0 0 11

via gray node

0.5 0 1 1 12


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Rated goals

Act

ions

Empirical data micro-foundation




Nodes

(intermediate) targets for agents lifetime costs/benefits (for agents,

nodes and “external”) multi-dimensional features

memory / payoffs status rights-of-use (technology) limits (short and long term)

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Edges

directed paths between nodes lifetime costs/benefits (for agents,

edge and “external”) multi-dimensional features

memory / payoffs rights-of-use (Technology) limits (short and long term)

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Technologies

Needed to pass nodes and edges lifetime purchase price costs/benefits-factor multi-dimensional absolute costs/benefits depend

on node/edge and technology

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Aggregating single actions

Complex systems huge number of actors Actors influencing their environment and each other Emergent macro behaviour macro result may differ from a (individually or politically)

desired behaviour

Control “… is an intentional intervention into a social or socio-

technical system aiming at changing it in an intentional way.” (Adelt et al. 2014)

by changing boundary conditions parameters like rights-of-use, costs, limits …

without knowing the result in advance

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Innovation policy / How to change the system

new technologies or change comparative

costs of existing technologies (by tax changes, subsidies etc.)

change the topology of

the net

create incentives

electric vehicles vs. SUVs add technologies change their parameters

build new streets, or streets usable for some technologies only

bike-parking facilities, charging stations for electric vehicles change node parameters

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To which extent is the system affected (or even changed) by interventions?

Depends on the type of intervention and

the strategies of the different types of actors.




Simulation of Urban Mobility (SUMO-S)

simulation of a traffic guidance system

modes of governance strong control soft control self-coordination

Adelt/Weyer/Fink, 2014: Controlling complex

systems. In: Ergonomics. Adelt/Weyer/Fink, 2014: Steuerung komplexer

Systeme. In: Soziale Welt.

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System stability (low numbers are better)

mix of drivers mean values of rows

mode of governance

Mix0 (only

obedient)

Mix1 (realistic)

Mix2 (more

obedient)

Mix3 (more single-

minded)

(Mix1 to Mix3)

strong control 5,858 8,011 9,340 9,308 8,886

soft control 6,301 10,187 5,820 9,591 8,533

self-coordination 8,500 10,064 10,181 10,603 10,283

mean value of columns 6,886 9,421 8,447 9,834

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Conclusion: SimCo

abstract representation of infrastructure systems ABMS with a sociological micro-foundation actor types, empirical foundation dynamic, emergent behaviour examine the effects of interventions in order to gain

more knowledge about the functioning of complex systems depends on actors’ reaction measure micro and macro performance

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Fabian Adelt – Johannes Weyer - ea-aw.de · PDF filetechnische universität dortmund...

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