scholarly journals The Boundaries of Cognition and Decision Making

2021 ◽  
pp. 93-112
Author(s):  
Toby Prike ◽  
Philip A. Higham ◽  
Jakub Bijak
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Empirical Evidence ◽  
Experimental Methodology ◽  
Agent Based Models ◽  
New Developments ◽  
Agent Based ◽  
Individual Level ◽  
Extended Discussion ◽  
Psychological Experiments

AbstractThis chapter outlines the role that individual-level empirical evidence gathered from psychological experiments and surveys can play in informing agent-based models, and the model-based approach more broadly. To begin with, we provide an overview of the way that this empirical evidence can be used to inform agent-based models. Additionally, we provide three detailed exemplars that outline the development and implementation of experiments conducted to inform an agent-based model of asylum migration, as well as how such data can be used. There is also an extended discussion of important considerations and potential limitations when conducting laboratory or online experiments and surveys, followed by a brief introduction to exciting new developments in experimental methodology, such as gamification and virtual reality, that have the potential to address some of these limitations and open the door to promising and potentially very fruitful new avenues of research.

scholarly journals High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach

2021 ◽  
Author(s):  
Wouter H. Vermeer ◽  
Justin D. Smith ◽  
Uri Wilensky ◽  
C. Hendricks Brown
Keyword(s):  
Decision Making ◽  
Social Systems ◽  
Open Science ◽  
Model Verification ◽  
High Fidelity ◽  
Agent Based ◽  
Individual Level ◽  
Adverse Health Outcomes ◽  
Modeling Process ◽  
Novel Method

AbstractPreventing adverse health outcomes is complex due to the multi-level contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM’s ability to do so depends strongly on its accuracy in capturing the phenomenon. Furthermore, for ABMs to be useful, they need to be accepted by and available to decision-makers and other stakeholders. These two attributes of accuracy and acceptability are key components of open science. To ensure the creation of high-fidelity models and reliability in their outcomes and consequent model-based decision-making, we present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication. Examples from HIV and overdose prevention work illustrate how these recommendations can be applied.

scholarly journals We predict a riot: inequality, relative deprivation and collective destruction in the lab (Dezecache, Allen, von Zimmermann & Richardson, in press)

2021 ◽  
Author(s):  
Guillaume Dezecache ◽  
James M. Allen ◽  
Jorina von Zimmermann ◽  
Daniel C. Richardson
Keyword(s):  
Collective Action ◽  
Psychological Factors ◽  
Relative Deprivation ◽  
Population Data ◽  
The Other ◽  
Agent Based Models ◽  
Agent Based ◽  
Individual Level ◽  
Psychological Mechanisms ◽  
Post Hoc

Riots are unpredictable and dangerous. Our understanding of the factors that cause riots are based on correlational observations of population data, or post hoc introspection of individuals. To complement these accounts, we developed innovative experimental techniques, investigated the psychological factors of rioting, and explored their consequences with agent-based simulations. We created a game, ‘Parklife’, that physically co-present participants played using smartphones. In two teams, participants tapped on their screen to grow trees and flowerbeds on separate but adjacent virtual parks. Participants could also tap to vandalise the other team’s park. In some conditions, we surreptitiously introduced inequity between the teams so that one (the disadvantaged team) had to tap more for each reward. The experience of inequity caused the disadvantaged team to engage in more destruction, and to report higher relative deprivation and frustration. Agent-based models suggested that acts of destruction were driven by the interaction between individual level of frustration and the team’s behaviour. Our results provide insights into the psychological mechanisms underlying collective action.

scholarly journals Representation of decision-making in European agricultural agent-based models

2018 ◽  
Vol 167 ◽  
pp. 143-160 ◽  
Author(s):  
Robert Huber ◽  
Martha Bakker ◽  
Alfons Balmann ◽  
Thomas Berger ◽  
Mike Bithell ◽  
...  
Keyword(s):  
Decision Making ◽  
Agent Based Models ◽  
Agent Based

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 We predict a riot: inequity, relative deprivation and collective destruction in the laboratory

2021 ◽  
Vol 288 (1959) ◽  
pp. 20203091
Author(s):  
Guillaume Dezecache ◽  
James M. Allen ◽  
Jorina von Zimmermann ◽  
Daniel C. Richardson
Keyword(s):  
Collective Action ◽  
Psychological Factors ◽  
Relative Deprivation ◽  
Population Data ◽  
The Other ◽  
Agent Based Models ◽  
Agent Based ◽  
Individual Level ◽  
Psychological Mechanisms ◽  
Post Hoc

Riots are unpredictable and dangerous. Our understanding of the factors that cause riots is based on correlational observations of population data, or post hoc introspection of individuals. To complement these accounts, we developed innovative experimental techniques, investigated the psychological factors of rioting and explored their consequences with agent-based simulations. We created a game, ‘Parklife’, that physically co-present participants played using smartphones. In two teams, participants tapped on their screen to grow trees and flowerbeds on separate but adjacent virtual parks. Participants could also tap to vandalize the other team's park. In some conditions, we surreptitiously introduced inequity between the teams so that one (the disadvantaged team) had to tap more for each reward. The experience of inequity caused the disadvantaged team to engage in more destruction, and to report higher relative deprivation and frustration. Agent-based models suggested that acts of destruction were driven by the interaction between individual level of frustration and the team's behaviour. Our results provide insights into the psychological mechanisms underlying collective action.

Decision Making in Agent-Based Models

2015 ◽  
pp. 370-378 ◽  
Author(s):  
Guillem Francès ◽  
Xavier Rubio-Campillo ◽  
Carla Lancelotti ◽  
Marco Madella
Keyword(s):  
Decision Making ◽  
Agent Based Models ◽  
Agent Based

Narrative analysis in economics as climbing complexity

2020 ◽  
pp. 5-30
Author(s):  
Vitaly L. Tambovtsev
Keyword(s):  
Decision Making ◽  
Empirical Research ◽  
Narrative Analysis ◽  
Agent Based Models ◽  
Agent Based ◽  
Basic Framework ◽  
Feasible Alternative ◽  
Effective Instrument

Two turns in economics during last decades are analyzed — complexity turn, and information turn, and the narrative analysis role for these turns realization is discussed. Basic framework of narrative analysis is described, and it is shown that its efficacy is limited by groups of individuals which have resources that give them possibilities to treat the narrative’s plot as a feasible alternative in decision-making situation. It is grounded that now agent-based models are the effective instrument for theoretical and empirical research under turns to complexity or information alike.

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