Aggregation in agent-based models of economies

2012 ◽  
Vol 27 (2) ◽  
pp. 151-162 ◽  
Author(s):  
Scott E. Page
Keyword(s):  
Fixed Points ◽  
Network Structure ◽  
Social Systems ◽  
Macro Level ◽  
Agent Based Models ◽  
Agent Based ◽  
Emergent Phenomenon ◽  
Learning Rules ◽  
Equilibrium Approach ◽  
Micro Level

AbstractAgent-based models are often described as bottom-up because macro-level phenomena emerge from the micro-level interactions of agents. These macro-level phenomena include fixed points, cycles, dynamic patterns, and long transients. In this paper, I explore the link between micro-level characteristics—learning rules, diversity, network structure, and externalities—and the macro-level patterns they produce. I focus on why we need agent-level modeling, on how these models produce emergent phenomenon, and on how agent-based models help understand outcomes of social systems in a way that differs from the analytic, equilibrium approach.

A CONNECTIONIST ABM OF SOCIAL CATEGORIZATION PROCESSES

2012 ◽  
Vol 15 (06) ◽  
pp. 1250077 ◽  
Author(s):  
DIRK VAN ROOY
Keyword(s):  
Social Influence ◽  
Network Structure ◽  
Social Adaptation ◽  
Multiple Sources ◽  
Agent Based ◽  
Individual Level ◽  
Extensive Research Program ◽  
Micro Level ◽  
The Individual ◽  
Over Time

This paper introduces a connectionist Agent-Based Model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes [D. Van Rooy, F. Van Overwalle, T. Vanhoomissen, C. Labiouse and R. French, Psychol. Rev. 110, 536 (2003)]. At the macro-level, these individual networks are combined into a "community of networks" so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them [B. Hazlehurst, and E. Hutchins, Lang. Cogn. Process. 13, 373 (1998)]. The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. In a extensive research program, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes.

scholarly journals Dynamic calibration of agent-based models using data assimilation

2016 ◽  
Vol 3 (4) ◽  
pp. 150703 ◽  
Author(s):  
Jonathan A. Ward ◽  
Andrew J. Evans ◽  
Nicolas S. Malleson
Keyword(s):  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Social Systems ◽  
Open Data ◽  
Dynamical Behaviour ◽  
Agent Based Models ◽  
Agent Based ◽  
Simple Question ◽  
Complex Social Systems ◽  
Using Data

A widespread approach to investigating the dynamical behaviour of complex social systems is via agent-based models (ABMs). In this paper, we describe how such models can be dynamically calibrated using the ensemble Kalman filter (EnKF), a standard method of data assimilation. Our goal is twofold. First, we want to present the EnKF in a simple setting for the benefit of ABM practitioners who are unfamiliar with it. Second, we want to illustrate to data assimilation experts the value of using such methods in the context of ABMs of complex social systems and the new challenges these types of model present. We work towards these goals within the context of a simple question of practical value: how many people are there in Leeds (or any other major city) right now? We build a hierarchy of exemplar models that we use to demonstrate how to apply the EnKF and calibrate these using open data of footfall counts in Leeds.

scholarly journals Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

2021 ◽  
Author(s):  
Gang Zhang ◽  
Hao Li ◽  
Rong He ◽  
Peng Lu
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Macro Level ◽  
Collective Actions ◽  
Agent Based ◽  
Individual Behaviors ◽  
Micro Level

AbstractThe outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

Dynamic Data Mining Based on the Stability of Dynamic Models

2021 ◽  
pp. 99-117
Author(s):  
Hocine Chebi
Keyword(s):  
Data Mining ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Fixed Points ◽  
Dynamic Models ◽  
Agent Based Models ◽  
Dynamic Data ◽  
Agent Based ◽  
The Monte Carlo Method ◽  
Evacuation System

This work presents a new approach based on the use of stable dynamic models for dynamic data mining. Data mining is an essential technique in the process of extracting knowledge from data. This allows us to model the extracted knowledge using a formalism or a modeling technique. However, the data needed for knowledge extraction is collected in advance, and it can take a long time to collect. The objective is therefore to move towards a solution based on the modeling of systems using dynamic models and to study their stability. Stable dynamic models provide us with a basis for dynamic data mining. In order to achieve this objective, the authors propose an approach based on agent-based models, the concept of fixed points, and the Monte-Carlo method. Agent-based models can represent dynamic models that mirror or simulate a dynamic system, where such a model can be viewed as a source of data (data generators). In this work, the concept of fixed points was used in order to represent the stable states of the agent-based model. Finally, the Monte-Carlo method, which is a probabilistic method, was used to estimate certain values, using a very large number of experiments or runs. As a case study, the authors chose the evacuation system of a supermarket (or building) in case of danger, such as a fire. This complex system mainly comprises the various constituent elements of the building, such as rows of shelves, entry and exit doors, fire extinguishers, etc. In addition, these buildings are often filled with people of different categories (age, health, etc.). The use of the Monte-Carlo method allowed the authors to experiment with several scenarios, which allowed them to have more data to study this system and extract some knowledge. This knowledge allows us to predict the future situation regarding the building's evacuation system and anticipate improvements to its structure in order to make these buildings safer and prevent the greatest number of victims.

Exploring Emergence within Social Systems with Agent Based Models

2014 ◽  
pp. 52-71 ◽  
Author(s):  
Marcia R. Friesen ◽  
Richard Gordon ◽  
Robert D. McLeod
Keyword(s):  
Modeling And Simulation ◽  
Real World ◽  
Research Group ◽  
Social Systems ◽  
Emergent Behavior ◽  
Agent Based Models ◽  
Social Modeling ◽  
Agent Based ◽  
Global Parameters ◽  
Representative Samples

In this chapter, the authors examine manifestations of emergence or apparent emergence in agent based social modeling and simulation, and discuss the inherent challenges in building real world models and in defining, recognizing and validating emergence within these systems. The discussion is grounded in examples of research on emergence by others, with extensions from within our research group. The works cited and built upon are explicitly chosen as representative samples of agent-based models that involve social systems, where observation of emergent behavior is a sought-after outcome. The concept of the distinctiveness of social from abiotic emergence in terms of the use of global parameters by agents is introduced.

scholarly journals Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Heike I. Brugger ◽  
Adam Douglas Henry
Keyword(s):  
Social Networks ◽  
Peer Effects ◽  
Social Systems ◽  
Scientific Basis ◽  
Virtual Space ◽  
Solar Pv ◽  
Agent Based Models ◽  
Agent Based ◽  
Incentive Programs ◽  
Feed In Tariff

Agent-based models are used to explore how social networks influence the effectiveness of governmental programs to promote the adoption of solar photovoltaics (solar PV) by residential households. This paper examines how a common characteristic of social networks, known as network segregation, can dampen the indirect benefits of solar incentive programs that arise from peer effects. Peer effects cause an agent to be more likely to adopt a technology if they are socially connected to other adopters. Due to network segregation, programs that target relatively affluent agents can generate rapid increases in overall adoption levels but at the cost of increasing disparities in access to solar technology between rich and poor communities. These dynamics are explored through theoretical agent-based models of solar adoption within hypothetical social systems. The effectiveness of three types of solar incentive programs, the feed-in tariff, leasing programs, and seeding programs, is explored. Even though these programs promote rapid adoption in the short term, results demonstrate that network segregation can create serious distributional justice problems in the long term for some programs. The distributional justice effects are particularly severe with the feed-in tariff. Overall, this paper provides an illustration of how agent-based models may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of policymaking.

scholarly journals Intra-group decision-making in agent-based models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Allegra A. Beal Cohen ◽  
Rachata Muneepeerakul ◽  
Gregory Kiker
Keyword(s):  
Decision Making ◽  
Functional Groups ◽  
Group Dynamics ◽  
Group Decision Making ◽  
Local Governments ◽  
Large Scale ◽  
Group Decision ◽  
Social Systems ◽  
Agent Based Models ◽  
Agent Based

AbstractMany agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women’s empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.

scholarly journals Statistical analysis of tipping pathways in agent-based models

2021 ◽  
Author(s):  
Luzie Helfmann ◽  
Jobst Heitzig ◽  
Péter Koltai ◽  
Jürgen Kurths ◽  
Christof Schütte
Keyword(s):  
Social Systems ◽  
Large Population ◽  
Dimensional Structure ◽  
Agent Based Models ◽  
Agent Based ◽  
Cascading Effects ◽  
Majority Opinion ◽  
Transition Path Theory ◽  
Natural Choice ◽  
Low Dimensional

AbstractAgent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

Agent-based models of bounded rationality

2017 ◽  
Vol 23 (1/2) ◽  
pp. 2-12 ◽  
Author(s):  
Davide Secchi
Keyword(s):  
Bounded Rationality ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Special Issue ◽  
Agent Based Models ◽  
Content Type ◽  
Agent Based ◽  
Team Research ◽  
Complex Adaptive ◽  
To Come

Purpose The purpose of this editorial is to introduce the Special Issue “Agent-Based Models of Bounded Rationality” and to provide an overview of its rationale and main objectives. Design/methodology/approach After outlining the overall framework to justify the choice of agent-based modeling in relation to bounded rationality, an overview of the six papers published in the Special Issue is presented. Findings The paper argues that simulation of complex adaptive social systems is a way to set the ground for updating the concept of bounded rationality and prepare for it to still play a significant role in the years to come. Originality/value After its introduction, bounded rationality remained mostly used but seldom discussed in both its assumptions and its meaning. The originality of this introduction is to unveil some of the points that keep rationality still at the core of organization and team research.

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