237,352 research outputs found
ACE Models of Endogenous Interactions
Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).Endogenous interaction, Agent-based Computational Economics (ACE)
Exploring a New ExpAce: The Complementarities between Experimental Economics and Agent-based Computational Economics
What is the relationship, if any, between Experimental Economics and Agent-based Computational Economics? Experimental Economics (EXP) investigates individual behaviour (and the emergence of aggregate regularities) by means of human subject experiments. Agent-based Computational Economics (ACE), on the other hand, studies the relationships between the micro and the macro level with the aid of artificial experiments. Note that the way ACE makes use of experiments to formulate theories is indeed similar to the way EXP does. The question we want to address is whether they can complement and integrate with each other. What can Agent-based computational Economics give to, and take from, Experimental Economics? Can they help and sustain each other, and ultimately gain space out of their restricted respective niches of practitioners? We believe that the answer to all these questions is yes: there can be and there should be profitable âcontaminationsâ in both directions, of which we provide a first comprehensive discussion.Experimental Economics, Agent-based Computational Economics, Agent-Based Models, Simulation.
Evoplex: A platform for agent-based modeling on networks
Agent-based modeling and network science have been used extensively to
advance our understanding of emergent collective behavior in systems that are
composed of a large number of simple interacting individuals or agents. With
the increasing availability of high computational power in affordable personal
computers, dedicated efforts to develop multi-threaded, scalable and
easy-to-use software for agent-based simulations are needed more than ever.
Evoplex meets this need by providing a fast, robust and extensible platform for
developing agent-based models and multi-agent systems on networks. Each agent
is represented as a node and interacts with its neighbors, as defined by the
network structure. Evoplex is ideal for modeling complex systems, for example
in evolutionary game theory and computational social science. In Evoplex, the
models are not coupled to the execution parameters or the visualization tools,
and there is a user-friendly graphical interface which makes it easy for all
users, ranging from newcomers to experienced, to create, analyze, replicate and
reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software
available at https://evoplex.org
Making Models Match: Replicating an Agent-Based Model
Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent-based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.Replication, Agent-Based Modeling, Verification, Validation, Scientific Method, Ethnocentrism
Adaptive microfoundations for emergent macroeconomics
In this paper we present the basics of a research program aimed at providing microfoundations to macroeconomic theory on the basis of computational agentbased adaptive descriptions of individual behavior. To exemplify our proposal, a simple prototype model of decentralized multi-market transactions is offered. We show that a very simple agent-based computational laboratory can challenge more structured dynamic stochastic general equilibrium models in mimicking comovements over the business cycle.Microfoundations of macroeconomics, Agent-based economics, Adaptive behavior
Agent-Based Modeling and its Tradeoffs: An Introduction & Examples
Agent-based modeling is a computational dynamic modeling technique that may
be less familiar to some readers. Agent-based modeling seeks to understand the
behaviour of complex systems by situating agents in an environment and studying
the emergent outcomes of agent-agent and agent-environment interactions. In
comparison with compartmental models, agent-based models offer simpler, more
scalable and flexible representation of heterogeneity, the ability to capture
dynamic and static network and spatial context, and the ability to consider
history of individuals within the model. In contrast, compartmental models
offer faster development time with less programming required, lower
computational requirements that do not scale with population, and the option
for concise mathematical formulation with ordinary, delay or stochastic
differential equations supporting derivation of properties of the system
behaviour. In this chapter, basic characteristics of agent-based models are
introduced, advantages and disadvantages of agent-based models, as compared
with compartmental models, are discussed, and two example agent-based
infectious disease models are reviewed
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
The Promises and Perils of Agent-Based Computational Economics
In this paper I analyse the main strengths and weaknesses of agent-based computational models. I first describe how agent-based simulations can complement more traditional modelling techniques. Then, I rationalise the main theoretical critiques against the use of simulation, which point to the following problematic areas: (i) interpretation of the simulation dynamics, (ii) estimation of the simulation model, and (iii) generalisation of the results. I show that there exist solutions for all these issues. Along the way, I clarify some confounding differences in terminology between the computer science and the economic literature.Agent-based, Simulation, Microsimulation, Computational Economics, Structural Estimation, Economic methodology
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