Agent-Based Modeling of Small-Scale Societies: State of the Art and Future Prospects

Author(s):  
Henry T. Wright

The thematic social sciences—economics, political science, psychology, and so on—often privilege that aspect of human action on which they focus. Can we fruitfully understand change in human affairs from the perspectives of these disciplines? Philosophers have (for millennia), and anthropologists and geographers (for little more than a century) have said "no," and have attempted to view human phenomena as a totality. Anthropology, a holistic discipline, at its best integrates human biology, cultural anthropology or ethnology, psychological anthropology, linguistics, and archaeology. But the task is daunting, and has led often to elegant, but very specific case studies. However, new theoretical approaches to nonlinear and adaptive systems and to modeling such approaches give hope that rigorous general formulations are possible. The Culture Group of the Santa Fe Institute focuses on long-term stability and transformation in cultural developments. In December 1997, with the support of the Wenner-Gren Foundation for Anthropological Research, a diversity of researchers gathered in Santa Fe to assess the progress of this working group and to chart future directions. We had many fruitful exchanges, ranging from general theoretical problems of cultural change and its explanation to the specifics of modeling actual cultural processes. The touchstones of the discussions were breakthroughs in the modeling of small-community networks in southwestern North America, but new developments in other theoretical and empirical areas also proved important in pointing toward future efforts. This volume presents the much discussed and revised papers from the Santa Fe meeting. The conference began, as does this volume, with overviews of the state of the art of modeling. George Gumerman, in his preface, touches on the roots of modeling whole social and cultural systems in North America, threads of inquiry which are picked up in many chapters of this volume. Tim Kohler, in his elegant introduction argues the advantages of agent-based modeling as the resolution of several outstanding problems in traditional social science. Nigel Gilbert then provides rich insight into recent work in Europe, little known to many North American social scientists outside the modeling community.

Author(s):  
Lin Qiu ◽  
Riyang Phang

Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to understand the dynamic interactions between political actors and extrapolate from the process of individual political decision making to collective outcomes. Agent-based modeling (ABM) offers a means to comprehend and theorize the nonlinear, recursive, and interactive political process. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions. It allows the specification of agent properties and rules governing agent interactions in a simulation to observe how micro-level processes generate macro-level phenomena. It forces researchers to make assumptions surrounding a theory explicit, facilitates the discovery of extensions and boundary conditions of the modeled theory through what-if computational experiments, and helps researchers understand dynamic processes in the real-world. ABM models have been built to address critical questions in political decision making, including why voter turnouts remain high, how party coalitions form, how voters’ knowledge and emotion affect election outcomes, and how political attitudes change through a campaign. These models illustrate the use of ABM in explicating assumptions and rules of theoretical frameworks, simulating repeated execution of these rules, and revealing emergent patterns and their boundary conditions. While ABM has limitations in external validity and robustness, it provides political scientists a bottom-up approach to study a complex system by clearly defining the behavior of various actors and generate theoretical insights on political phenomena.


Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).


2012 ◽  
Vol 163 (10) ◽  
pp. 396-400 ◽  
Author(s):  
Roland Olschewski ◽  
Oliver Thees

Chances and limits of the analysis of wood markets Recent approaches of behavioural economics and agent-based modeling can enhance knowledge about market processes and results and widen the focus for the assessment of future market developments by emphasising the individual behaviour of market participants and scenario techniques. In this article we resume possible contributions of the particular approaches to better describe, explain and forecast real market developments. The exposition is based on state-of-the-art knowledge and reflects insights gained during the 8th Forest Economic Seminar in autumn 2011, where researchers and practitioners presented their findings.


2021 ◽  
Vol 11 (21) ◽  
pp. 10397
Author(s):  
Barry Ezell ◽  
Christopher J. Lynch ◽  
Patrick T. Hester

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.


As part of the SFI series, this book presents the most up-to-date research in the study of human and primate societies, presenting recent advances in software and algorithms for modeling societies. It also addresses case studies that have applied agent-based modeling approaches in archaeology, cultural anthropology, primatology, and sociology. Many things set this book apart from any other on modeling in the social sciences, including the emphasis on small-scale societies and the attempts to maximize realism in the modeling efforts applied to social problems and questions. It is an ideal book for professionals in archaeology or cultural anthropology as well as a valuable tool for those studying primatology or computer science.


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Muaz A. Niazi

The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems.


2017 ◽  
Vol 41 (1) ◽  
pp. 170-176 ◽  
Author(s):  
Matthieu Guillemain ◽  
Johan Elmberg ◽  
Claire A. Pernollet ◽  
Celine Arzel ◽  
John M. Eadie

Sign in / Sign up

Export Citation Format

Share Document