Keywords

value of information; decisions; decision making; biases; natural resources; hazards; environmental risks; water quality; gamification; participatory modeling

Start Date

5-7-2022 12:00 PM

End Date

8-7-2022 9:59 AM

Abstract

A multitude of behavioral and social factors control how people/institutions/collectivities (i.e., hereafter called actors) perceive, process, value, communicate, and use (or not) information in decision making. These factors include various forms of discounting, in-group/out-group processes, biases, beliefs, heuristics, values (including moral and social norms) – as well as information perception, processing and communication issues. Game theory, metagame theory, drama theory, decision theory, agent-based modeling, role-playing and other experimental games are some of the ways that have been used to model and predict how different actors react to information and use it in decision making. Some of these techniques may be used to understand and explore the factors controlling decision making a posteriori. Nonetheless, attempts at predictive modeling are challenged by non-linearities, high sensitivities to unknown or poorly understood factors, dynamic contexts and histories, and a multiplicity of dynamically changing actors. One challenge is that actors sometimes respond viscerally to events but are also capable of critical thinking and adaptation. Also, adaptive behavior change by actors depends on feedbacks, often delayed or diffused in intensity. Furthermore, socially extended cognition dynamically affects actors, both in their definition and through actor interactions with each other. Actors also have limitations in accurately perceiving and processing both biophysical and social information; and in accurately assigning causal responsibility for behavioral consequences. We explore many of the social and behavioral factors that modulate the construction and use of information in decision making through several use cases. Our examples will include modeling and decision making for both chronic and acute issues across a range of spatio-temporal scales (e.g., Chesapeake Bay; pandemic perceptions/responses; lake water quality in Colorado and New Zealand; fire and flood hazards.) Our work seeks to challenge the iEMSs community to improve modeling and gamification of behavioral and social factors – and thereby advance decision making for the management of natural resources, hazards,

Stream and Session

false

COinS
 
Jul 5th, 12:00 PM Jul 8th, 9:59 AM

Perceptions, information prioritizations, decision making – Can they be modeled and gamified?

A multitude of behavioral and social factors control how people/institutions/collectivities (i.e., hereafter called actors) perceive, process, value, communicate, and use (or not) information in decision making. These factors include various forms of discounting, in-group/out-group processes, biases, beliefs, heuristics, values (including moral and social norms) – as well as information perception, processing and communication issues. Game theory, metagame theory, drama theory, decision theory, agent-based modeling, role-playing and other experimental games are some of the ways that have been used to model and predict how different actors react to information and use it in decision making. Some of these techniques may be used to understand and explore the factors controlling decision making a posteriori. Nonetheless, attempts at predictive modeling are challenged by non-linearities, high sensitivities to unknown or poorly understood factors, dynamic contexts and histories, and a multiplicity of dynamically changing actors. One challenge is that actors sometimes respond viscerally to events but are also capable of critical thinking and adaptation. Also, adaptive behavior change by actors depends on feedbacks, often delayed or diffused in intensity. Furthermore, socially extended cognition dynamically affects actors, both in their definition and through actor interactions with each other. Actors also have limitations in accurately perceiving and processing both biophysical and social information; and in accurately assigning causal responsibility for behavioral consequences. We explore many of the social and behavioral factors that modulate the construction and use of information in decision making through several use cases. Our examples will include modeling and decision making for both chronic and acute issues across a range of spatio-temporal scales (e.g., Chesapeake Bay; pandemic perceptions/responses; lake water quality in Colorado and New Zealand; fire and flood hazards.) Our work seeks to challenge the iEMSs community to improve modeling and gamification of behavioral and social factors – and thereby advance decision making for the management of natural resources, hazards,