Agent-based models

Winter Simulation Conference, 2005

Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

Oxford Bibliographies in Sociology, 2017

Agent-Based Modeling is a research method that represents theories of social behavior as computer programs of a particular kind, rather than narratives (as ethnography does) or equations (as regression models do). Like existing research methods in sociology (both qualitative and quantitative) it can be applied throughout the discipline and offers advantages for certain research questions. In particular, the approach is referred to as agent-based because the computer program unambiguously represents interactions between heterogeneous social actors while also explicitly determining their aggregate simulated consequences. This distinguishes Agent-Based Modeling from existing quantitative approaches in sociology where the relationship between aggregate associations and individual agency is often unclear. It also distinguishes the method from existing qualitative approaches that, while investigating individuals and their interactions, have no systematic techniques for establishing their aggregate consequences. Given this capability, the methodology of Agent-Based Modeling has a distinctive logic. Agent-Based Models are calibrated using data on individual behavior (for example using ethnography or laboratory experiments) and then the computer program generates simulated aggregate data. This can then be compared with equivalent real data for validation. It is the independence of these two activities that provides Agent-Based Modeling with its distinctive claim to explanatory power. The explicitly represented link between individual and aggregate respects the complexity of social systems, the phenomenon in which individuals and their simple interactions may produce surprisingly counter-intuitive aggregate outcomes. Agent-Based Models are thus particularly suitable for investigating sociological issues involving heterogeneous actors, diverse cognitive processes and social systems mediated by entities operating between the level of the individual and the aggregate (like schools and churches).

2011

The currently fashionable modelling tool agent-based simulation is characterized. The first part concerns the past. It presents a selection of the major intellectual roots from which this new tool emerged. It is important for social scientists, in particular for economists, to see that two relevant impacts came from neighbouring disciplines: biology and network theory. The second part concerns the present of ABM. It aims at highlighting the essential features which are characteristic for an agent-based model. Since there are currently several different opinions on this topic, the one presented here also includes some more epistemologically oriented ideas to support its plausibility. In particular the notion of emergence is scrutinized and extended. This part ends with a short recipe stating how to build an agent-based model. In the last part some ideas on the future of agent based modelling are presented. This part follows the sequence of syntax, semantics, and pragmatics. The syntactic challenges, like operators for pattern recognition, will be meat by a continuing variety of software packages and programming languages tailored to support ABM. The semantic aspect of future agent-based modelling hinges on the close relationship between the tool ABM and its object of investigation, e.g. evolutionary political economy. The need to model institutional change or communication processes will imply adaptive evolution of ABM. The pragmatics of future agent-based modelling are finally characterized as the most demanding – but also as the most influential – element that the new tool will bring about.

Frontiers in Psychology, 2014

In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.

Computational Complexity, 2012

H ow is it possible that a whole ancient civilization disappeared? Was this caused by climate changes? What type of recruitment strategy among social insects is best adapted to their particular environment? How much time does it take to evacuate an airport if people have limited perception caused by smoke as well as restricted mobility? What if many of these people travel in groups or families? How long does it take for commuters to reach their destinations if an important arterial in the Los Angeles area is closed? These are the kind of questions that can be and are answered by agent-based modeling and simulation (ABMS). In this paradigm, simulated human beings or animals are modeled as agents, interacting with some of their peers as well as with their environment. The environment, as in many multiagent systems, plays a key role and must therefore be carefully taken into account. For instance, passengers seeking to leave the airport just mentioned try to find the shortest way to an exit, which may be partially hindered by debris. These are only some examples of scenarios-also characterized as complex adaptive systems-that can be investigated using ABMS. The core idea here is to use simulated agents for producing a phenomenon that shall be analyzed, reproduced, or predicted. This generative, bottom-up nature of modeling and simulation provides great potential for dealing with problems in which conventional modeling and simulation paradigms have difficulties capturing the core features of the original system. In what follows, this particular modeling and simulation paradigm, its concept, properties, and application are introduced Articles

Lecture Notes in Computer Science, 2006

This thesis proposal aims to provide a new approach to the study of complex adaptive systems in social sciences through a methodological framework for modeling and simulating these systems like artificial societies. Agent based modeling (ABM) is well fitted for the study of social systems as it focuses on how local interactions among agents generate emergent larger and global social structures and patterns of behavior. The issues addressed by our framework are presented as well as its most important components.

2005

The term agent, deriving from the Latin "agens", identifies someone (or something) who acts; the same word can also be used to define a mean through which some action is made or caused. The term is used in many different fields and disciplines, such as economics, physics, natural sciences, sociology and many others. In computer science, the word is used to define very heterogeneous entities and sometimes is even abused. The main purpose of this work is to investigate various kinds of software agents that could be applied to modeling and simulation of complex social systems.

Global Economics and Management Review, 2013

Agent-based modeling and simulation (ABS) is emerging as a key technology that is helping to enhance the understanding of social sciences. Systems ranging from organizations to economies and societies can be modeled to provide insights in ways that were previously not possible with quantitative approaches. The Sentient World Simulation (SWS) is an ultra-large-scale ABS developed to capture a comprehensive view of "Whole of Government" operations. The SWS supports a strategic geopolitical perspective that captures the interplay between military operations and the social, political, and economic landscapes. The SWS consists of a synthetic environment that mirrors the real world in all its key aspects. Models of individuals within the synthetic world represent the traits and mimic the behaviors of their real-world counterparts. As models influence each other and the shared synthetic environment, behaviors and trends emerge in the synthetic world as they do in the real world. The SWS reacts to actual events and incorporates newly sensed data from the real world into the virtual environment. Trends in the synthetic world can be analyzed to validate alternate worldviews. The SWS provides an open, unbiased environment in which to implement diverse models. This results in a single holistic framework that integrates existing theories, paradigms, and courses of action.

Proceedings of the Annual Meeting of the Cognitive Science Society, 2005

The term agent, deriving from the Latin "agens", identifies someone (or something) who acts; the same word can also be used to define a mean through which some action is made or caused. The term is used in many different fields and disciplines, such as economics, physics, natural sciences, sociology and many others. In computer science, the word is used to define very heterogeneous entities and sometimes is even abused. The main purpose of this work is to investigate various kinds of software agents that could be applied to modeling and simulation of complex social systems.