Modelling Human Behaviour in Agent-Based Models

Kennedy, William G.

doi:10.1007/978-90-481-8927-4_9

Cited by 71 publications

(31 citation statements)

References 10 publications

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“…on Helbing, 2012;Meyer et al, 2009;Tesfatsion and Judd, 2006), and identifies cognitive, affective, social and norm consideration and learning as the key dimensions in describing and comparing human decision-making in ABM. A similar classification can also be found in Kennedy (2012).…”

Section: Description Of Decision-making In Abmsupporting

confidence: 56%

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

Huber

Bakker

Balmann

et al. 2018

Agricultural Systems

129

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 Agent-based modelling is a suitable tool for improving the understanding of farmers' behaviour.  Review 20 agricultural ABM addressing heterogeneous decision-making processes in the context of European agriculture.  Considerable scope to improve diversity in representation of decision-making by combining existing modelling approaches.  More coordinated and purposeful combinations of ABM and hybrid modelling approaches are needed.  Results provide an entry point for collaboration of agent-based modellers, agricultural systems modellers and social scientist.

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Section: Description Of Decision-making In Abmsupporting

confidence: 56%

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

Huber

Bakker

Balmann

et al. 2018

Agricultural Systems

129

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“…There is considerable heterogeneity regarding the way agent behavior is modeled in ABMs of climate mitigation policy. Different authors suggest distinct ways to classify behavior and decision‐making (e.g., Balke & Gilbert, 2014; Kennedy, 2012; Schlüter et al, 2017). Grimm et al (2006) proposed a protocol—Overview, Design Concepts and Details (ODD)—for reporting ABM studies, which was extended by Müller et al (2013) to include 10 design concepts related to human decision‐making.…”

Section: Agents and Their Behavior In The Reviewed Studiesmentioning

confidence: 99%

A review of agent‐based modeling of climate‐energy policy

Castro

Drews

Exadaktylos

et al. 2020

WIREs Climate Change

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Agent‐based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behavior than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and nonmarket interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate‐energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation, and emissions trading through adoption subsidies to information provision tools such as smart meters and eco‐labels. Our treatment pays specific attention to behavioral assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy questions. This article is categorized under: Climate Economics > Economics of Mitigation

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“…We call it rationality level, which has been included to fulfill our second requirement of facilitating to set an equilibrium between the rational and the affective sides. Including the affective state and personality aspects on the filter process relies on theories that argue for the need of considering the influence of emotions and individual differences for behaving either rationally or emotionally/intuitively/unconsciously, in order to properly model human behavior [26]. Besides, experimental studies offer evidences that emotions drive deliberative decision making [13].…”

Section: Core Processes Of An Affective Bdi Agentmentioning

confidence: 99%

Toward Formal Modeling of Affective Agents in a BDI Architecture

Alfonso

Vivancos

Botti

2017

ACM Trans. Internet Technol.

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Affective characteristics are crucial factors that influence human behavior, and often the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents that simulate humans' reasoning in equilibrium with their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an agent architecture (GenIA 3 ). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, and the extension of the agent language (Jason) to support affection-based reasoning, as well as the parametrization of the affectionrationality equilibrium.

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Modelling Human Behaviour in Agent-Based Models

Cited by 71 publications

References 10 publications

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

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

A review of agent‐based modeling of climate‐energy policy

Toward Formal Modeling of Affective Agents in a BDI Architecture

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