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Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung

Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences. From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences. Java codes and a standalone software package will be presented in the book for easy use for those not familiar with mathematical details

With the turbulence in global trade and the necessity to develop non-commodity exports in Russia, the choice of an effective strategy for exporters' conduct in the food market is becoming vitally important these days. Agent-based models simulating the behavior of decentralized self-learning agents with their own goals and capabilities can be used as the tools for analyzing and predicting market movements. The paper presents the results of the agent-based approach to market modeling by the example of barley, which is one of the top commodity items of the Russian food exports on
the basis of FAOSTAT and Rosstat data for the period 1997–2017. As a result of the study, an agent-based model of the world barley market was built, and a series of calculated experiments was carried out in the AnyLogic development environment with the changes in such factors as the level of global demand, the amount of customs duties, the exporting companies' funds in order to determine the strategic conduct of the exporting agents taking into account the limited rationality of the participants in communicative interaction. The proposed approach develops modern theory of consumer behavior and simulation, and the results of the study can be used in the formation of development programs
for the Russian agro-food exports.

This paper builds on and extends a classic paper (hereafter referred to as F–O) published by Masahisa Fujita and Hideaki Ogawa in 1982. Their paper models the emergence of urban centers brought about by household and firm location decisions in the context of spatially differentiated labor and land market interactions. Their approach is an analytical one that seeks to characterize the equilibrium values of the system. In contrast, we employ an agent-based modeling (ABM) approach that seeks to replicate the individual household and firm behaviors that lead to equilibrium or nonequilibrium outcomes. The F–O model has little to say about what happens outside of equilibrium, while the ABM approach is preoccupied with this question and is particularly well suited to address questions of path dependency and bounded rationality that lie well beyond the scope of the F–O original. We demonstrate that the urban outcomes that emerge depend critically upon the bidding behavior of agents and the institutional context within which their decisions are made.

This article studies the effect of different implementation approaches on agent-based computer models. This is accomplished via four reimplementations of a simple model of self-organization. How implementation choices "guide our hands" and lead possibly to implicit assumptions about the modeled system is also demonstrated. Furthermore, the question of what makes a model agent based is studied. An argument is made that agent-based implementation is rather a matter of degree than a binary choice.

Agent-based modeling has become a common and well-established tool in the social sciences and certain of the humanities. Here, we aim to provide an overview of the different modeling approaches in current use. Our discussion unfolds in two parts: we first classify different aspects of the model-building process and identify a number of characteristics shared by most agent-based models in the humanities and social sciences; then we map relevant differences between the various modeling approaches. We classify these into different dimensions including the type of target systems addressed, the intended modeling goals, and the models' degree of abstraction. Along the way, we provide reference to related debates in contemporary philosophy of science.

To predict which supply chain effects will appear when applying governmental control policies, infrastructure investments, and business strategies, multi-agent-based simulation (MABS) can be used. In this paper, we identify abstract supply chain responsibilities, roles and interactions that are argued to be sufficient for representing all types of organizations involved in the processes of buying and selling products and transport services. The identified responsibilities, roles and interactions are organized into a framework together witha set of modeling guidelines, which we relate to the GAIAmethodology to simplify the process of developing multi-agent-based supply chain simulation models. To illustrate the usage of the framework, we provide two case studies where we apply it to two different MABS models.

This book addresses increasingly relevant social problems enabled by social media through the lens of system science research and modeling. Instead of evaluating existing patterns from existing social media data, the book focuses on exploring the possibility of various policies. Specifically, it provides fresh insight into the political echo chamber and polarization in social media using agent-based modeling and scenario analysis. The book contains a practical framework to study the factors influencing echo chambers and polarization formation occurring in social media communication. By modeling individual social media users' information consumption, the influence of various behaviors and policies are captured as macro-phenomena. The book also introduces a comprehensive agent-based reinterpretation of the Zaller model, a classic in public opinion research. In addition, the book demonstrates two real-life applications of the model using empirical observations: resolving the conflicting observations of the online echo chamber effect, and modeling the influence of vaccine-related Facebook pages on users' opinions about vaccination.

The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach -- with hundreds of examples and exercises using NetLogo -- enables readers to begin constructing models immediately, regardless of experience or discipline. The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.