Endogenous Social Networks from Large-Scale Agent-Based Models

2017 ◽  
Author(s):  
Eric Tatara ◽  
Nicholson Collier ◽  
Jonathan Ozik ◽  
Charles Macal
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Agent Based Models ◽  
Agent Based

Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

2012 ◽  
pp. 301-326
Author(s):  
Mitchell Welch ◽  
Paul Kwan ◽  
A.S.M. Sajeev ◽  
Graeme Garner
Keyword(s):  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Livestock Disease ◽  
Interacting Agents ◽  
National Model ◽  
Complex Phenomena ◽  
Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

COMPUTATIONAL FEATURES OF AGENT-BASED MODELS

2005 ◽  
Vol 02 (01) ◽  
pp. 33-48 ◽  
Author(s):  
MASSIMO BERNASCHI ◽  
FILIPPO CASTIGLIONE
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Agent Based Modeling ◽  
System Response ◽  
Agent Based Models ◽  
Design Data ◽  
Agent Based ◽  
Data Structures And Algorithms ◽  
Immune System Response ◽  
Large Scale Simulations

Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

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.

Recordkeeping and Human Evolution

2006 ◽  
Vol 20 (3) ◽  
pp. 201-229 ◽  
Author(s):  
Sudipta Basu ◽  
Gregory B. Waymire
Keyword(s):  
Human Brain ◽  
Division Of Labor ◽  
Human Evolution ◽  
Evolutionary Theory ◽  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Weights And Measures ◽  
Language And Law ◽  
Evolutionary Role

We seek to characterize the evolutionary role played by the transactional record that is the foundation of modern accounting. We theorize that systematic recordkeeping crystallizes memory and, along with other institutions (e.g., law, weights, and measures), promotes the trust necessary for large-scale human cooperation. Our theory yields two predictions: (1) permanent records emerge to supplement memory when complex intertemporal exchange between strangers becomes more common and (2) systematic records and other exchange-supporting institutions co-evolve and feed back to increase gains from economic coordination and division of labor. Many aspects of ancient Mesopotamian recordkeeping are consistent with these hypotheses, suggesting that our evolutionary theory is plausible. We outline ways to directly test our predictions with experiments, ethnographies, and agent-based models, and describe other techniques that can be used to explore the co-evolution of accounting with the human brain, language, and law.

scholarly journals Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks

2015 ◽  
Vol 125 ◽  
pp. 203-213 ◽  
Author(s):  
J. Zhang ◽  
L. Tong ◽  
P.J. Lamberson ◽  
R.A. Durazo-Arvizu ◽  
A. Luke ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Overweight And Obesity ◽  
Agent Based Models ◽  
Agent Based

scholarly journals Creativity in temporal social networks: how divergent thinking is impacted by one’s choice of peers

2020 ◽  
Vol 17 (171) ◽  
pp. 20200667
Author(s):  
Raiyan Abdul Baten ◽  
Daryl Bagley ◽  
Ashely Tenesaca ◽  
Famous Clark ◽  
James P. Bagrow ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Divergent Thinking ◽  
Self Organization ◽  
Agent Based Simulation ◽  
Web Based ◽  
Agent Based ◽  
High Performing ◽  
Simulation Parameters

Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials ( N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers’ ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network.

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