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This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming.Recent scientific and technological advances have created exciting opportunities for such improvements.

This chapter summarizes our view of priority challenges for social‐behavioral modeling. Some challenges are inherent: individuals behave in complicated ways and social systems are complex adaptive systems (CAS). Such systems pose wicked problems for policy‐makers. Other challenges relate to specific scientific problems. We discuss a selection of these: rebalancing the portfolio of models and methods, confronting uncertainty, synthesis, the dynamic of connecting theory and data, and redefining model validity. We summarize briefly elements of a way ahead.

This chapter draws on lessons from other domains to suggest ways for social‐behavioral modeling to be useful in aiding high‐level decision‐making on both national security and social‐policy issues. Analysis to aid such decision‐making often requires addressing issues from a system perspective, providing both a broad, low‐resolution view and selected high‐resolution views, confronting deep uncertainty, and finding strategies that are flexible to changes of mission or objective, adaptive to circumstances, and robust to events to the extent feasible with available resources. Such analysis needs to draw on a portfolio of methods and tools. The chapter discusses implications for the nature of social‐behavioral modeling.

With the growth of big data, there has been a rush to develop models in the social sciences that extract patterns of behavior from data without a corresponding theory to explain those behaviors. While it is useful to identify and examine these patterns, the use of these regularities for predictions and intervention is dangerous without theory. In this chapter, I explore this danger and develop guidelines for avoiding the pitfalls of theoryless big data modeling. I also examine two methods (parameter optimization and rule induction) of using agent‐based modeling to create theory‐interpreted knowledge about large data sets. I also discuss future extensions that make this a potentially revolutionary approach to next‐generation computational social science.

A great deal of creativity and innovation will be needed to surmount the considerable challenges. Even describing these challenges remains a subject for scholarly debate. The chapter provides a list of challenges that should be considered representative rather than comprehensive. The challenges are complexity of human issues, fragmentation, representations, and applications of social‐behavioral modeling. In the information age, our understanding of the human condition is deepening with new ways to observe, experiment, and understand behavior. The need for accuracy and precision in describing the understanding of the human condition will require models with structured descriptions of data sources, data interpretations, and related assumptions. Since modeling is an attempt to represent and exploit knowledge, it needs foundations in science. In the domain of social‐behavioral work, the theory and models are overly narrow ‐ focusing on some particular variables and ignoring others. The chapter also presents an overview of the key concepts discussed in this book.

The past several years have seen advances in the area of simulation for social‐behavioral modeling. Nevertheless, in many ways, the field has not come of age. This paper argues that the field is being hampered by a lack of understanding by proponents and detractors of the nature of social‐behavioral simulation and the underlying challenges for advancement of the area.

When people ask – and provide resources – to develop a computational model of some phenomena of interest, why do they do this? What are they expecting? How good are we at meeting their expectations? Do the things we create help them to make better decisions? Do these things demonstrably influence their decisions? This chapter addresses these questions in the context of domains where behavioral and social phenomena are central to the problems of interest. We explore these questions in the domains of healthcare delivery, higher education, and urban systems, contrasting these explorations with endeavors in aerospace and defense, electronics and semiconductors, and other complicated engineered systems where human roles are important but prescribed. We contrast these endeavors and discuss insights gleaned from numerous successes and quite a few less than successful endeavors.

Model development is an incremental and iterative process, but it often prematurely converges to a single supposedly authoritative model. Better use of scientific method involves a dynamic, iterative process in which diverse hypotheses and combinations thereof are compared with evidence. The goal may be to find a set of models and variants thereof with different structures and to have an understanding of when to use. In this chapter, I describe an abstract architecture to guide the dynamic and iterative inquiry. The intent is to identify the different functions that it would be useful to accomplish. These include management functions associated with the designing experiments and composing models for theory–data comparison and scientific discovery as with inferring causal relationships and aiding the researcher along the way in various other ways. I take a systems approach that envisions a special class of “cognitive models” to assist the process. These have self‐awareness features. The intent is that this conceptual architecture will assist researchers as they prepare for complicated experiments comparing models with evidence, i.e. that it will alert them to the need for mechanisms to accomplish the functions discussed.

This chapter reviews the current state of the art in data infrastructure and artificial intelligence approaches that could be valuable for social and behavioral modeling. Among the newer machine learning methods, adversarial training and fuzzy cognitive maps seem to have particular unrealized potential. The chapter then discusses the troublesome theory–data gap: the mismatch between measurable data streams and meaningful explanatory theories to frame the data. The chapter identifies this issue as a key barrier to meaningful social and behavioral modeling. It then discusses the need to move from purely data‐driven work to theory‐informed work and to tighten the iterative loop between theory and data analysis. Closing the theory–data gap is a general problem. The chapter illustrates three example models that attempt to integrate theory‐informed and data‐driven modeling: a network model, a factor‐tree model, and a fuzzy cognitive map model. The first model addresses meme transmission. The last two address the public support for terrorism. The chapter identifies key questions and challenges along the way. These are questions that a notional social and behavioral modeling research community will need to tackle as it grows.

Utility maximization and related optimization algorithms are important features of social‐behavioral models, but cultural (and genetic) inheritance places constraints on, enables, and sets the context for any such maximizing behavior. It makes sense to represent culture as important “inherited information.” Inheritance processes create culture‐related correlations in empirical data (“Galton's problem”) that present challenges for standard statistical models like multiple regression. In this chapter, I demonstrate that we can now resolve such problems with new computational methods. It is therefore time to routinely incorporate cultural considerations in social‐behavioral modeling. Doing so, however, will require a good deal of cultural data that has been rarely collected (e.g. reliable data on religion and values).