Claudio Cioffi-Revilla

MASON: A Multiagent Simulation Environment

MASON is a fast, easily extensible, discrete-event multi-agent simulation toolkit in Java, designed to serve as the basis for a wide range of multi-agent simulation tasks ranging from swarm robotics to machine learning to social complexity environments. MASON carefully delineates between model and visualization, allowing models to be dynamically detached from or attached to visualizers, and to change platforms mid-run. This paper describes the MASON system, its motivation, and its basic architectural design. It then compares MASON to related multi-agent libraries in the public domain, and discusses six applications of the system built over the past year which suggest its breadth of utility.

Computational Social Science

The social sciences investigate human and social dynamics and organization at all levels of analysis (consilience), including cognition, decision making, behavior, groups, organizations, societies, and the world system. Computational social science is the integrated, interdisciplinary pursuit of social inquiry with emphasis on information processing and through the medium of advanced computation. Themain computational social science areas are automated information extraction systems, social network analysis, social geographic information systems (GIS), complexity modeling, and social simulation models. Just like Galileo exploited the telescope as the key instrument for observing and gaining a deeper and empirically truthful understanding of the physical universe, computational social scientists are learning to exploit the advanced and increasingly powerful instruments of computation to see beyond the visible spectrum of more traditional disciplinary analyses.

Initial Social Complexity in Southwestern Asia: The Mesopotamian Advantage

The emergence of early Mesopotamian (Sumerian) civilization must be understood within the framework of the unique ecology and geography of the alluvial lowlands of the Tigris and Euphrates Rivers during the late 5th and 4th millennia B.C. The former gave Mesopotamian societies important advantages in agricultural productivity and subsistence resource resilience not possessed by contemporary polities on their periphery, while the latter gave them enduring transportational advantages. This material imbalance created opportunities and incentives that made it both possible and probable that early Mesopotamian elites would use trade as one of their earliest and most important tools to legitimize and expand their unequal access to resources and power. Given this, a still hypothetical but testable) model is presented that accounts for the precocious socioeconomic differentiation and urban growth of southern Mesopotamia in the 4th millennium as social multiplier effects inadvertently set in motion by evolving trade patterns. This trade was first largely internal, between individual southern polities exploiting rich but localized ecological niches within the Mesopotamian alluvium during the Late Ubaid and Early Uruk periods. By the Middle and Late Uruk periods, however, inherently asymmetrical external trade between growing southern cities and societies at their periphery in control of coveted resources gained more prominence. In due course, import-substitution processes further amplified the one-sided socio-evolutionary impact on southern societies of these shifting trade patterns. Unequal developmental rates resulting from the operation of these processes over time explain why the earliest complex societies of southwestern Asia appeared in southern Mesopotamia and not elsewhere.

Agent-Based Modeling Simulation of Social Adaptation and Long-Term Change in Inner Asia

We present a new international project to develop temporally and spatially calibrated agent-based models of the rise and fall of polities in Inner Asia (Central Eurasia) in the past 5,000 years. Gaps in theory, data, and computational models for explaining long-term sociopolitical change—both growth and decay—motivate this project. We expect three contributions: (1) new theoreticallygrounded simulation models validated and calibrated by the best available data; (2) a new long-term cross-cultural database with several data sets; and (3) new conceptual, theoretical, and methodological contributions for understanding social complexity and long-term change and adaptation in real and artificial societies. Our theoretical framework is based on explaining sociopolitical evolution by the process of “canonical variation”.

A Canonical Theory of Origins and Development of Social Complexity

ABSTRACT The puzzle of origins and future of government and social complexity in human and social dynamics, arguably a characteristic feature of the emergence and long-term evolution of hierarchy and power in the history of civilizations, is an enduring topic that has challenged political scientists, anthropological archaeologists, and other social scientists and historians. This paper proposes a new computational theory for the emergence of social complexity that accounts for the earliest formation of systems of government (pristine polities) in prehistory and early antiquity, as well as present and future political development. This general social theory is based on a “fast process” of crisis and opportunistic decision-making through collective action, which feeds a “slow” process of political development or decay. The “fast” core iterative process is “canonical” in the sense that it undergoes variations on a recurring theme of signal detection, information-processing, problem-solving, successful adaptation and occasional failure. When a group is successful in managing or overcoming serious situational changes (stresses or opportunities, endogenous or exogenous, social or physical) a probabilistic phase transition may occur, under a specified set of conditions, yielding a long-term (slow) probabilistic accrual process of emergent sociopolitical complexity and development. A reverse process may account for decay. The canonical theory is being formally implemented through the “PoliGen” agent-based model (ABM), based on the new Multi-Agent Simulator of Networks and Neighborhoods (MASON). Empirically, the theory is testable with the datasets on polities developed by the Long-Range Analysis of War (LORANOW) Project. This paper focuses on the concepts, mechanisms, and basic formal structure that constitute the canonical theory and inform the subsequent simulation model.

Replication of Sugarscape Using MASON

The purpose of this research was to replicate the Sugarscape model (Eptstein and Axtell 1996) and simulation outcomes as described in Growing Artificial Societies (GAS). Sugarscape is a classic agent-based model and contemporary simulation toolkits usually only have a very simple replication of a few core rules. There is scant evidence of significant replication of the rules and simulation outcomes; code supplied with Repast, Swarm, and NetLogo implement a minority of the rules in Sugarscape. In particular, the standard Repast distribution only implements Growback, Movement, and Replacement. Sugarscape implementations in these toolkits are clearly provided only as basic demonstrations of how wellknown social models might be implemented, rather than complete achievements of scientific replication.

On the Likely Magnitude, Extent, and Duration of an Iraq-Un War

Results from this study conducted in November 1990 at the Long-Range Analysis of War Project, University of Colorado, predict that the magnitude μ of a war between Iraq and the United Nations collective security coalition will be between 5 (hundred thousand combatant fatalities) and 6- (low million). War magnitude is defined as the common logarithm of total combatant fatalities. This falsifiable forecast (“an event 5 ≤μ≤ 6- will occur”) was derived using mathematical models for fatalities, extent, and duration, estimated on historical populations of Correlates of War Project data on interstate wars involving great powers. The forecast will be wrong if and only if 5 > μ > 6-. However, failure is scientifically unlikely in this case, because the models are parsimonious, they show strong empirical fit, and extensive tests prove that they are largely insensitive to historical evolution - unlike Lanchester-type models.

Simplicity and reality in computational modeling of politics

Modeling a polity based on viable scientific concepts and theoretical understanding has been a challenge in computational social science and social simulation in general and political science in particular. This paper presents a computational model of a polity (political system) in progressive versions from simple to more realistic. The model, called SimPol to highlight the fundamental structures and processes of politics in a generic society, is developed using the combined methodologies of object-based modeling (OOM), the Unified Modeling Language (UML), and the methodology of Lakatos’ research programs. SimPol demonstrates that computational models of entire political systems are methodologically feasible and scientifically viable; they can also build on and progress beyond previous theory and research to advance our understanding of how polities operate across a variety of domains (simple vs. complex) and levels of analysis (local, national, international). Both simple and realistic models are necessary, for theoretical and empirical purposes, respectively.

Evolution of Maya Polities in the Ancient Mesoamerican System

The analysis of politics in antiquity presents new opportunities for political science and international relations, particularly the ancient New World (c. 2000 B.C. to A.D. 1521). Governance through leadership and institutions, collective action, war and peace, alliance dynamics, regional hegemonies, interstate rivalries, and other universal patterns of world politics existed in Meso-America, antedating the modern state system. We report findings from a study to record systematically the rise and fall of Maya polities in the Meso-American political system, using sources from archaeology and epigraphy. Based on tests of competing hypotheses and new distribution statistics and hazard rates (survival analysis) for 72 Maya polities, our findings support a model of Maya political dynamics based on pre-classic origins, punctuated phases of development, multiple cycles of system expansion and collapse, and weaker political stability for increasingly complex polities. We draw two main implications: (a) a new theory of the Maya political collapse(s), based on their failure to politically integrate; and (b) confirmation for a new periodization of Maya political evolution, different from the traditional cultural periodization, based on several cycles of rise-and-fall, not just one. Our findings may also make possible future investigations in areas such as the war-polarity and war-alliances hypotheses.

The MASON HouseholdsWorld Model of Pastoral Nomad Societies

Computational modeling of pastoralist societies that range as nomads over diverse environmental zones poses interesting challenges beyond those posed by sedentary societies. We present HouseholdsWorld, a new agent-based model of agro-pastoralists in a natural habitat that includes deserts, grasslands, and mountains. This is the paper-of-record for the HouseholdsWorld model as part of a broader interdisciplinary project on computational modeling of long-term human adaptations in Inner Asia. The model is used for conducting experiments on socio-environmental interactions, social dynamics experiments, and for developing additional models with higher levels of social complexity.