Research Description for Service Oriented Industries

8/14/2019 Research Description for Service Oriented Industries

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Center for Future Banking (CFB) -Applied Research Discovery and

Innovation (ARDI)

2009

Applied Research Discovery and Innovation (ARDI) is CFBs five-year initiative tocreate breakthroughs in Banking, Financial Services, the marketplace environment, withoutcomes made possible by innovations and advances in computational creative thinkingusing scientific and social-economic-technology research in a broad range of applications. Computational creative thinking is defined comprehensively to encompasscomputational concepts, creativity, methods, techniques, models, simulations, algorithms,aesthetics, design, practices, and tools, that augment human cognition, understanding, anddecision making using computing devices. Computational creative thinking merged withSocial-economic science provides a context to define a Service Science which may helpinform innovations in service oriented industries such as Banking and Finance and itsassociated adjacent industries. ARDI research and education outcomes are expected to

produce paradigm shifts in our understanding of a wide range of complex phenomena andsocio-economic-technical innovations that create new wealth for, commercial,customers, and shareholder interest, and enhance service quality for customers,suppliers, partners and associates, while addressing the risk of evolving and innovatingthe Banking industry, thus expanding the understanding of the entire ecosystem of theglobal Banking industry. The research would use Banking as a context but produceoutcomes that help define Service Science and would be generally applicable to anyservice industry.

ARDI investigators are expected to help define a new discipline of Service Science andgenerate ground breaking multidisciplinary research outcomes across banking, finance,computer and information sciences, engineering, mathematical sciences, social sciences,

behavioral, and economic sciences, cognitive sciences, human computer interaction,architecture, product design, business, law, and any related discipline that has ainterdependence. Related industries may also be explored such as health care,gerontology, housing, life long learning, financial literacy, consumer advocacy,government policy, workforce productivity, human resource management, training and

development, marketing science, as these are integral and adjacent to banking and wealthcreation and management. The development of computational creative thinking toenable discovery and innovation on all fronts of science and technology is likely tostimulate advances that collectively accelerate development.

With an emphasis on bold, multidisciplinary activities that, through computational

rgarcia at media.mit.edu March 23, 2009
mailto:[email protected]:[email protected]


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creative thinking, promise radical, paradigm-changing outcomes, ARDI promotestransformative research within CFB. Accordingly, investigators are encouraged to cometogether in the development of far-reaching, high-risk research agendas that capitalize oninnovations in, and/or innovative use of, computational creative thinking to create new

knowledge and societal impact far beyond todays capabilities. Research efforts aroundthe world are beginning to address various aspects of the CFB Macro themes, and ARDI

projects are expected to build upon productive intellectual partnerships involvinginvestigators from academia, industry, government, and/or other types of organizations,including international entities, that advance ARDI objectives within the rapidly evolvingglobal context. The research and education should have a particular focus on real-worldexperimentation and application while being grounded in theory and practice. CFBencourages the use of novel or hybrid approaches that fuse together different disciplineswhere both qualitative and quantitative research methods are applied.

Motivated by transformative research opportunities, ARDI seeks bold proposals within or across the following CFB Macro Themes and three capability areas:

The following are the Macro Themes with broad descriptions that may encompass manydisciplines.

Macro Theme Description

InformationValue Flow Information flow is the signal versus and noise of information science. The valueis an increase or decrease in signal. Information is the lowest atomic unit of measure for our research. The flows form interdependent chains or graph of relationships. The flows of value are not exclusive to money and include anyconvertible value.

BehavioralEconomics

Includes any behaviors that happen before and after the decision of a consumer or producer. Behavior is observed, modeled, anticipated, projected, and predicted aswell as all the vagaries of the human condition . The unit of measure is at humanscale and includes many uncontrolled factors.

Identity, TrustPrivacy, Security

Identity includes concepts of privacy or public disclosure. Trust implies theconcepts of gradations of security or no security if full trust is granted. Bothincluded measures of credibility and honesty or value systems that are human.

The concepts span system processes and human interaction. Network

Economies Networks are the systems that connect and the people acting in social interactions.Information flows, social behavior, identity and trust are aggregated into economicinteractions within a network.

SocialResponsibility

Includes individual and corporate actions representing and raises questions of ethics. This aggregates the network economies into defined groups that care for,direct and indirect impact, and consequences of decisions .

The Macro Themes are described in the broadest terms to allow for interpretation by the


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Innovation (ARDI)

2009

investigators and the proposed research. These Macro Themes are to be defined by theresearch and serve to illustrate a wide range of interest. Many traditional academicdisciplines may be described as being applicable to one or more of the above MacroThemes, it is our intention that this interpretation take place and encourage an expansiveview of the proposals in terms of cross discipline application of concepts and work fromareas that would not typically be considered part of what Banking my desire to consider.For example, one could adopt concepts from biology or ecology and attempt to applythem to an understanding of dynamics in financial systems and it may be an acceptable

proposal. What would be required is a creative use of computation to help model thedynamic and simulate its representational validity to banking and finance. In additionsome aspect should involve how human activities impact the dynamic with computation

augmenting the system. The investigator should take liberties in interpreting the MacroThemes and attempt to describe their proposal using some of these terms.

To illustrate one possible interpretation of the Macro Themes the chart below suggest anexample of a consumer impact, theoretic basis, and research unit for each Macro Theme.These are in not absolute nor would they apply to every interpretation and are onlyincluded to help the reader formulate their own view or what these Macro Themes mayaddress or how they might be associated with particular research areas, methods or techniques. Note that while computation may not be at the core of each entry it may beappropriate to instrument processes to capture data such that computation could be usedfor the analysis where some creative interpretation of the results may be needed to arriveat a conclusion or subsequent line of inquiry.

Macro Theme Consumer Impact Theory Basis Research UnitInformationValue Flow

UsabilitySensing Store

CommunicationsSignal/Noise

Bits/Bytes Numbers/Time

BehavioralEconomics

Consumer Buying Decision

IndividualChoice Theory

PsychologyMind/Emotion

Identity, TrustPrivacy, Security

Consumer Confidence

Risk ContainmentChaos Theory

Safe SystemsSemiotics

Network Economies

ConnectedConsumption

Complexity/Game/Graph Theory

SocietalKnowledge

SocialResponsibility

Consumer Literacy

PhilosophyPolitics/Economics

ValuesEthics/Law



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The following are the capabilities areas that span the Macro Themes for which proposalsmay be submitted.

From Data to Information to Knowledge: enhancing human cognition and generating new knowledge from a wealth of digital data.

An abundance of digital data promises a profound impact in both the quality and rate of discovery and innovation in Service Science, as well as in other societal contexts.Worldwide, researchers are producing, accessing, analyzing, integrating and storingmassive amounts of digital data daily, through observation, experimentation andsimulation, as well as through the creation of collections of digital representations of tangible artifacts and specimens. Modern experimental and observational instrumentsgenerate and collect large sets of data of varying types (numerical, video, audio, textual,

multi-modal, multi-level, multi-resolution) at increasing speeds. Often, the data users arenot the data producers, and they thus face challenges in harnessing data in unforeseen andunplanned ways. In many applications, for example, in meso-scale financial predictionor critical infrastructure protection applications, the ability to gather, organize, analyze,model, and visualize large, multi-scale, heterogeneous data sets in rapid fashion is oftencrucial.

New methods are required that convert raw data into meaningful information that creates

knowledge and understanding from an abundance of digital data, and that accelerate thetransformation of knowledge into new products and services that stimulate economicgrowth as well as other societal benefits. Driven by compelling research opportunities,new efforts to support the complex tasks of exploratory data analysis and discovery must

be explored.

The massive scale and often dynamic nature of data dictate that relevant computationaltechnologies be fast, flexible, and capable of operating at multiple levels of abstraction.Data of different types often must be synthesized into a single model that permits anemphasis on data meaning rather than on the forms in which the data were originallyrepresented. Models may dynamically incorporate information via data assimilation andmachine learning. Alternative models may be compared in exploratory data analysis. Akey component of developing a model is often an inverse problem: deducing system

properties and structures, parameter values, or underlying principles from data. Inverse problems are commonly non-unique or in some way ill-posed, so that the data may notdetermine a unique model and selection of the best model may require careful


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Innovation (ARDI)

2009

optimization. Ultimately, the value of a model depends on the major challenge of validation against "ground truth"; feedbacks between mathematical, computational, andapplication-domain analysis, each influencing the next step in the others, are vital to real-world insight.

Analysis of large data sets, both real-time and offline, including numeric, textual, andtemporal data, demands scalable storage, manipulation, algorithms, presentation, whosecomputational complexity grows as slowly as possible with the scale of the data.Research may require the development of novel algorithms and strategies that, for example, can discern and exploit parametric, geometric, and topological properties of

data, as well as the development of novel data mining and dimension reductionmethodologies that can expose the knowledge underlying data. Some of the importantways of extracting information from data include data aggregation and annotation, patternrecognition, perturbation and sensitivity analysis, real-time manipulation, filtering andestimation, spectral graph analysis, statistical analysis, and stochastic simulation. Newvisualization methods can enhance human cognition, allowing scientists, engineers,researchers, to detect and comprehend previously indiscernible abstract concepts,

patterns, and important exceptions amidst vast data. Approaches informed by knowledgeof human cognition and perception can amplify individuals capability to perceive,understand, synthesize and reason about complex and often dynamic data. In somedomains, innovative technologies may also need to address the data confidentiality,

privacy, security, provenance, and regulatory issues that often impact the use of data.



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Understanding Complexity in Economic Models, System Dynamics, and FinancialEcology: deriving fundamental insights on systems comprising multiple interacting elements in natural, built, and social systems.

Identifying general principles and laws that characterize complexity and capture theessence of complex systems is one of the major challenges of 21 st century science.Complex systems are more than just complicated; they display distinct characteristics notencountered in "simple" systems, such as multi-scale interactions, emergent behavior,

pattern formation, and self-organization, and they are often inherently stochastic or operate in unpredictable settings. The traditional economic models that are the basis for modern banking needs to evolve to a system dynamic and in the near future a holisticfinancial ecology to address the global challenges of a banking industry that has a scalethat is so complex that the typical management strategies are now inadequate tounderstand, predict, and control the systems consequences. Nonlinear couplings and

feedbacks across multiple processes and scales typify these systems. They are notamenable to reductionism; finding constructs that persist through the dynamics isfundamental, and involves a major role for innovative computational experimentation.As well as advancing Service Science, the understanding of complexity will enable thedesign, synthesis, and control of novel complex engineered and human systems.Furthermore, it will facilitate intervention in and analysis of complex natural and socialsystems. This capability therefore promotes the exploration and modeling of naturalinteractions, connections, complex relations, and interdependencies, scaling from sub-

particles to galaxies, from sub-cellular to biosphere, and from the individual to thesocietal, across time, in order to understand, mimic, synthesize, and exploit complexsystems.

The functionalities offered by computational creative thinking allow experiments totake place entirely in cyber-space with models that simulate the real-world behavior. Inmany situations, simulation through computation is the only feasible approach to asystematic investigation of realistic complex service science phenomena, or is essential tothe scientific basis for and design of "traditional" experiments. Key challenges includeaccuracy and resolution, efficiency, perturbation analysis, uncertainty, stochasticity,

validation against ground truth, long-term dynamics, and predictive modeling.Simulations and computational experiments in mainstream environments within industryor informal educational settings can engage students, the public, commerce, in theexcitement of research discovery and innovation.

Much of the understanding of complexity will come from mathematical and statistical


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Innovation (ARDI)

2009

modeling and analysis, based on both theoretical and empirical studies. Mimicking andsynthesizing systems will exploit a wide variety of techniques. Complexity oftenrequires advances in numerical methods for differential, algebraic, and discrete systems.Other approaches include agent-based modeling, neural networks, and dynamicallyinteractive human-in-the-loop calculations. An important consideration for large systemsis that scalable methods and tools be available in the working environments.

Understanding Human Interaction, Behavior, and Relationship Networks byBuilding Virtual Self-Organizing Environments: enhancing discovery and innovationby bringing people and resources together across institutional, geographical and cultural boundaries, this should be inclusive of industry, government, academia, the public, theinternational community, the 3 rd world through the developed world.

Virtual Self-Organizing Environment (VSOE) can facilitate the conduct of cutting-edge,transformative research and learning within and across all fields. As complex, networkedsocio-economic-technical systems supported by cyber-infrastructure, VSOEs promise toconnect people and resources across institutional and geographic boundaries, to foster dynamic configurations of instruments, data streams, facilities, and researchers and toenable new approaches to Service Science inquiry through remote access to experimentaltools, observational instruments, simulation systems, and globally dispersed individuals.Because they extend beyond traditional brick and mortar research institutions, theyallow for more flexible boundaries and memberships and for scientific inquiry to be

performed at a scale and a distance never before possible. Achieving such radicalscalability and seamless integration and inter-operability will require the application of computational creative thinking to all levels of VSOE design, implementation, andmaintenance. A living lab of systems and human activity captured for research.

For example, how can researchers who remain rooted in their home institutions anddisciplines establish common ground for successful VSOEs that satisfy everyones

preferences and practices? How can heterogeneous data of different forms and types be

most effectively transferred and integrated? How can information systems become easilyinter-operable and accessible any place, any time? How can organizational andregulatory structures be aligned with their virtual ones? How can cultural differences beresolved? Myriad other concerns must be addressed in order to design, build, andadvance effective VSOEs. As distributed, dynamic, and computationally-enhancedmodes of operation and organization, VSOEs will need to overcome traditional

boundaries in unprecedented ways and not only expand but diversify the research



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opportunities. As such, VSOE's should also be explored as a primary vehicle for enhancing innovation and broadening participation in not just research but also excitinginquiry-based commerce.

Understanding how to model and leverage these socio-economic-technical systems togenerate and accelerate transformative research within and across different ServiceScience areas necessarily requires the bringing together of domain scientists withexpertise in, for example, economics, information science, network science, artificialintelligence and machine learning, game theory, work-flow and value chain analysis,statistics, software/hardware design, information privacy and security, participatory andsocial computing, operations research, human behavior, and organizational studies.Accordingly, ARDI investigators of different disciplinary perspectives should collaborateon the design, development, and implementation of VSOE's to test and verify proposedtheories and models of distributed learning and discovery with specific problems,

populations and purposes. In effect create a living lab in the environment for which theresearch may be applied, that is real-world settings.

Successful projects in the Virtual Organizations capability should seek to produce paradigm-shifting research in the targeted areas as well as advance the understanding of virtual organizations as new modalities of Service Science and commerce. The outcomesshould be transformative to create more generalized systematic knowledge and principled

understanding of the intertwined human behaviors and technological conditions thatenable effective VSOEs.

All three capabilities mentioned above are inter-related. Realistic modeling to accuratelyrepresent living systems are becoming possible for ever more complex phenomena,which defy understanding by other means. Such models and increasingly sophisticatedService Science observations are described in terms of data of unprecedented scale, fromwhich insight must be extracted through more ingenious techniques than before.

Attacking these problems also requires larger organizations, often both geographicallydispersed and intellectually diverse; empowerment of such groups is central to thistransformation. Accordingly, proposals in one capability, or that cross two or more of thethree capabilities, and are applicable to one of more of the Macro Themes areencouraged.

REVISION NOTES


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Innovation (ARDI)

2009

Initial draft, copied from the NSF Cyber-Infrastructure Discovery and Innovation description andsimplified and revised for CFB requirements. Portions of the Program Description are from the original

NSF Cyber-Infrastructure Discovery and Innovation grant application re-interpreted for CFB.


Research Description for Service Oriented Industries

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