Evolutionary Model Discovery: Automating Causal Inference for Generative Models of Human Social Behavior

The desire to understand the causes of complex societal phenomena is fundamental to the social sciences. Society, at a macro-scale has many measurable characteristics in the form of statistical distributions and aggregate measures; data which is increasingly abundant with the proliferation of online social media, mobile devices, and the internet of things. However, the decision-making processes and limits of the individuals who interact to generate these statistical patterns are often difficult to unravel. Furthermore, multiple causal factors often interact to determine the outcome of a particular behavior. Quantifying the importance of these causal factors and their interactions, which make up a particular decision-making process, towards a societal outcome of interest helps extract explanations that provide a deeper understanding of social behavior. Holistic, generative modeling techniques, in particular agent-based modeling, are able to \u27grow\u27 artificial societies that replicate emergent patterns seen in the real world. Driving the autonomous agents of these models are rules, generalized hypotheses of human behavior, which upon validation against real-world data, help assemble theories of human behavior. Yet often, multiple hypothetical causal factors can be suggested for the construction of these rules. With traditional agent-based modeling, it is often up to the modeler\u27s discretion to decide which combination of factors best represent the rule at hand. Yet, due to the aforementioned lack of insight, the modeled agent rule is often one out of a vast space of possible rules. In this dissertation, I introduce Evolutionary Model Discovery, a novel framework for automated causal inference, which treats such artificial societies as sandboxes for rule discovery and causal factor importance evaluation. Evolutionary Model Discovery consists of two major phases. Firstly, a rule of interest of a given agent-based model is genetically programmed with combinations of hypothesized factors, attempting to find rules which enable the agent-based model to more closely mimic real-world phenomena. Secondly, the data produced through genetic programming, regarding the correspondence of factor presence in the rule to fitness, is used to train a random forest regressor for importance evaluation. Besides its scientific contributions, this work has also led to the contribution of two Python open-source software libraries for high performance computing with NetLogo, Evolutionary Model Discovery and NL4Py. The results of applying Evolutionary Model Discovery for the causal inference of three very different cases of human social behavior are discussed, revisiting the rules underlying two widely studied models in the literature, the Artificial Anasazi and Schelling\u27s Segregation, and an ensemble model of diffusion of information and information overload. First, previously unconsidered factors driving the socio-agricultural behavior of an ancient Pueblo society are discovered, assisting in the construction of a more robust and accurate version of the Artificial Anasazi model. Second, factors that contribute to the coexistence of mixed patterns of segregation and integration are discovered on a recent extension of Schelling\u27s Segregation model. Finally, causal factors important to the prioritization of social media notifications under loss of attention due to information overload are discovered on an ensemble of a model of Extended Working Memory and the Multi-Action Cascade Model of conversation

Multiobjective genetic programming can improve the explanatory capabilities of mechanism-based models of social systems

The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science

Spatial agent-based modelling

Archaeologists were among some of the earliest users of agent-based modelling, but recent years have undoubtedly seen a surge of interest in the use of this technique to infer past behaviour or help develop new theories and methods. Although ABM software is much easier to use than it was even 20 years ago and sufficiently powerful computers are more readily available, the success of a modelling project is still largely determined by decisions made about the purpose and design of the model, and the subsequent experimental regime. This chapter guides the reader through those key issues. It covers epistemological topics such as the role of the model in a wider project, the trade-off between realism and generality, the idea of generative modelling and the importance of adequate experimentation. It also discusses technical issues such as options for the integration of ABM and GIS, and even the dangers inherent in poor design decisions about the scheduling of agent behaviour

Provisions of labor : prehistoric evidence for economic behavior at Ghost Ranch, New Mexico.

Excavations at Ghost Ranch of two hunter-gatherer rock shelters, GR-2 and GR-145, offer new opportunities for assessing prehistoric land-use in the Piedra Lumbre Basin of northcentral New Mexico. Intersite analysis of these remains provides new data for understanding subsistence organization during the Southwestern Archaic and Formative periods. Faunal and floral assemblages from the two sites, located five km apart and overlapping chronologically, suggest divergent patterns of resource collection, processing, and use by groups taking up temporary residence in a seasonal round. I argue that economic agency provides the best explanation for the sites’ differentiated remains. Furthermore, I propose that, based on this and related evidence, the divisions of labor characteristic of agricultural societies might have their origin in specialized behaviors practiced in discrete locations by prehistoric foraging peoples

Simulating prehistoric population dynamics and adaptive behavioral responses to the environment in Long House Valley and Black Mesa, Arizona

Dr. Lisa Sattenspiel, Dissertation Supervisor.Field of study: Anthropology."May 2018."This project contributes to our understanding of human adaptability to environmental stress and climate change in Long House Valley and Black Mesa, Arizona from AD 800-1350. This was accomplished through the development of a series of agentbased archaeological models. The first stage, Disaggregation, created a model that simulated individual persons within the Long House Valley landscape, a departure from the household-level models common in archaeological modeling. The second stage, Demography, applied empirically derived fertility and mortality rates to these human populations to provide insight into the effects of such rates on population patterns. The final stage expanded the modeled environment to include Black Mesa and allowed for the migration of individuals and households between the two areas in response to varying environmental and demographic pressures throughout the study period. The results of this project indicate that the introduction of biological and ethnographic realism to a model can produce unexpected results, including those that deviate from the population patterns observed archaeologically. Despite these unexpected interactions, the results support the importance of variations in agricultural productivity in driving human migrations in the region. Future archaeological models should consider further exploration small-scale, local population movements and the effects of dynamically changing fertility and mortality rates.Includes bibliographical references (pages 145-156)

Paleonutrition

The study of paleonutrition provides valuable insights into shifts and changes in human history. This is the most comprehensive book on the topic. Intended for students and professionals, it describes the nature of paleonutrition studies, reviews the history of research, discusses methodological issues in the reconstruction of prehistoric diets, presents theoretical frameworks frequently used in research, and showcases examples in which analyses have been successfully conducted on prehistoric individuals, groups, and populations. It offers an integrative approach to understanding state-of-the-art anthropological dietary, health, and nutritional assessments. The most recent and innovative methods used to reconstruct prehistoric diets are discussed, along with the major ways in which paleonutrition data are recovered, analyzed, and interpreted. The book includes five contemporary case studies that illustrate the mutually beneficial linkages between ethnography and archaeology

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

Faculty Publications and Creative Works 1997

One of the ways we recognize our faculty at the University of New Mexico is through this annual publication which highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 1997 calendar year. Faculty Publications and Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new of ideas. In support of this, UNM faculty during 1997 produced over 2,770 works, including 2,398 scholarly papers and articles, 72 books, 63 book chapters, 82 reviews, 151 creative works and 4 patents. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico. Nasir Ahmed Interim Associate Provost for Research and Dean of Graduate Studie

Can social norms explain long-term trends in alcohol use? Insights from inverse generative social science

Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact