Towards an Integrated Agent and Environment Architecture for Simulation of Human Decision Making and Behavior

Both Herbert A. Simon and Anthony Downs borrowed heavily from psychology to develop more accurate theories of Administrative Behavior outside and Inside Bureaucracy: Simon, to explicate the cognitive shortcomings in human rationality and its implications; and Downs, to argue that public officials, like other human beings, vary in their psychological needs and motivations and, therefore, behave differently in similar situations. I examine how recent psychological research adds important nuances to the psychology of human decision-making and behavior and points in somewhat other directions than those taken by Simon and Downs. Cue-taking, fast and intuitive thinking, and emotions play a larger role in human judgment and decision-making than what Simon suggested with his notion of bounded rationality. Personality trait theory provides a more general and solid underpinning for understanding individual differences in behavior, both inside and outside bureaucracy, than the 'types of officials' that Downs discussed. I present an agenda for a behavioral public administration that takes key issues in cognitive psychology and personality psychology into account.

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The Role of Executive Function in Shaping Reinforcement Learning

10.31234/osf.io/9cvw3 ◽

2020 ◽

Cited By ~ 1

Author(s):

Milena Rmus ◽

Samuel McDougle ◽

Anne Collins

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Instrumental Behavior ◽

Complex Environments ◽

Neural Computations ◽

Human Decision ◽

Brain And Behavior ◽

And Behavior ◽

The Brain

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of instrumental behavior are assumed to be supported by the brain’s executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

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COSIMO: A cognitive simulation model of human decision making and behavior in accident management of complex plants

IEEE Transactions on Systems Man and Cybernetics ◽

10.1109/21.179844 ◽

1992 ◽

Vol 22 (5) ◽

pp. 1058-1074 ◽

Cited By ~ 51

Author(s):

P.C. Cacciabue ◽

F. Decortis ◽

B. Drozdowicz ◽

M. Masson ◽

J.-P. Nordvik

Keyword(s):

Decision Making ◽

Simulation Model ◽

Cognitive Simulation ◽

Accident Management ◽

Human Decision ◽

And Behavior

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Hierarchies of Motivation Predict Individuals’ Attitudes and Values: A Neuropsychological Operationalization of the Five Factor Model

10.31234/osf.io/b94cx ◽

2021 ◽

Author(s):

Cameron Berg

Keyword(s):

Decision Making ◽

Factor Model ◽

Five Factor Model ◽

Machine Learning Algorithms ◽

Trade Off ◽

Trade Offs ◽

Human Decision ◽

Individual Decisions ◽

And Behavior ◽

Recurrent Patterns

Scientific investigations of human personality are concerned with uncovering recurrent patterns of behavior, valuation, and cognition across time. The Five Factor Model (FFM), commonly known as the “Big 5,” is considered the most scientifically rigorous consolidation of the components of human decision-making and behavior. This research presents a novel hypothesis for systematizing the factors of the FFM into a series of emotional, motivational, and intellectual trade-offs. 193 adult participants completed an online decision-making battery composed of scenarios generated in accordance with each superordinate trade-off framework. Machine learning algorithms were subsequently implemented to assess whether a participant’s individual “score” was able to predict their independently-reported attitudes related to political affiliation, gender identity, career preferences, economic beliefs, political correctness, spirituality, and others. Across every attitude probed, the trade-off framework presented in this research was able to more strongly predict a participant’s response than any other model or scale that could be found in the literature. This research strongly supports the utility of conceptualizing individual decisions, preferences, values, and motivations through the lens of the FFM-based trade-off frameworks outlined in this work.

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Hierarchies of Motivation Predict Individuals’ Attitudes and Values: A Neuropsychological Operationalization of the Five Factor Model

10.31234/osf.io/wk6tx ◽

2021 ◽

Author(s):

Cameron Berg

Keyword(s):

Decision Making ◽

Factor Model ◽

Five Factor Model ◽

Machine Learning Algorithms ◽

Trade Off ◽

Trade Offs ◽

Human Decision ◽

Individual Decisions ◽

And Behavior ◽

Recurrent Patterns

Scientific investigations of human personality are concerned with uncovering recurrent patterns of behavior, valuation, and cognition across time. The Five Factor Model (FFM), commonly known as the “Big 5,” is considered the most scientifically rigorous consolidation of the components of human decision-making and behavior. This research presents a novel hypothesis for systematizing the factors of the FFM into a series of emotional, motivational, and intellectual trade-offs. 193 adult participants completed an online decision-making battery composed of scenarios generated in accordance with each superordinate trade-off framework. Machine learning algorithms were subsequently implemented to assess whether a participant’s individual “score” was able to predict their independently-reported attitudes related to political affiliation, gender identity, career preferences, economic beliefs, political correctness, spirituality, and others. Across every attitude probed, the trade-off framework presented in this research was able to more strongly predict a participant’s response than any other model or scale that could be found in the literature. This research strongly supports the utility of conceptualizing individual decisions, preferences, values, and motivations through the lens of the FFM-based trade-off frameworks outlined in this work.

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Evaluating Human Decision-Making Theories With Monkey Data

PsycEXTRA Dataset ◽

10.1037/e633262013-157 ◽

2013 ◽

Author(s):

Scott D. Brown ◽

Pete Cassey ◽

Andrew Heathcote ◽

Roger Ratcliff

Keyword(s):

Decision Making ◽

Human Decision

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Evidence Supporting Theory of Shannon Capacity in Human Decision-Making

PsycEXTRA Dataset ◽

10.1037/e633262013-272 ◽

2013 ◽

Author(s):

Laurence T. Maloney ◽

James Tee ◽

Hang Zhang

Keyword(s):

Decision Making ◽

Shannon Capacity ◽

Human Decision

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History among the Junipers: Hosea 14:2–10 as Metahistoriography

Biblische Zeitschrift ◽

10.30965/25890468-06301006 ◽

2019 ◽

Vol 63 (1) ◽

pp. 105-116

Author(s):

Mark W. Hamilton

Keyword(s):

Decision Making ◽

Moral Formation ◽

Divine Agency ◽

Human Decision

Abstract The dual endings of Hosea promoted reflection on Israel’s history as the movement from destruction to restoration based on Yhwh’s gracious decision for Israel. It thus clarifies the endings of the prior sections of the book (chs. 3 and 11) by locating Israel’s future in the realm of Yhwh’s activities. The final ending (14:10) balances the theme of divine agency in 14:2–9 with the recognition of human decision-making and moral formation as aspects of history as well. The endings of Hosea thus offer a good example of metahistoriography, a text that uses non-historiographic techniques to speak of the movements of history.

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