scholarly journals Human behavior inside and outside bureaucracy: Lessons from psychology

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Asbjørn Sonne Nørgaard
Keyword(s):  
Decision Making ◽  
Psychological Research ◽  
Psychological Needs ◽  
Human Beings ◽  
Public Officials ◽  
Personality Psychology ◽  
Intuitive Thinking ◽  
Human Decision ◽  
Key Issues ◽  
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.

scholarly journals Algorithms as discrimination detectors

2020 ◽  
Vol 117 (48) ◽  
pp. 30096-30100 ◽  
Author(s):  
Jon Kleinberg ◽  
Jens Ludwig ◽  
Sendhil Mullainathan ◽  
Cass R. Sunstein
Keyword(s):  
Decision Making ◽  
Human Beings ◽  
Regulatory Systems ◽  
Human Decision ◽  
The Right

Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect—and hence to help prevent—discrimination.

The Role of Executive Function in Shaping Reinforcement Learning

2020 ◽  
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.

Implementation of an Intelligent Model for Decision Making Based on CBR for Supply Chain Solution in Retail for a Cluster of Supermarkets

2019 ◽  
pp. 381-397
Author(s):  
Adrian F. Loera-Castro ◽  
Jaime Sanchez ◽  
Jorge Restrepo ◽  
Angel Fabián Campoya Morales ◽  
Julian I. Aguilar-Duque
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Intelligent Systems ◽  
Case Based Reasoning ◽  
Human Beings ◽  
Human Decision ◽  
Decision Making Processes ◽  
A Chain ◽  
The Times ◽  
Intelligent Model

The latter includes customizing the user interface, as well as the way the system retrieves and processes cases afterward. The resulting cases may be shown to the user in different ways, and/or the retrieved cases may be adapted. This chapter is about an intelligent model for decision making based on case-based reasoning to solve the existing problem in the planning of distribution in the supply chain between a distribution center and a chain of supermarkets. First, the authors mentioned the need for intelligent systems in the decision-making processes, where they are necessary due to the limitations associated with conventional human decision-making processes. Among them, human experience is very scarce, and humans get tired of the burden of physical or mental work. In addition, human beings forget the crucial details of a problem, and many of the times are inconsistent in their daily decisions.

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

1992 ◽  
Vol 22 (5) ◽  
pp. 1058-1074 ◽  
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

scholarly journals Old Facts, New Beginnings: Thinking with Arendt about Algorithmic Decision-Making

2021 ◽  
pp. 1-23
Author(s):  
Lisa Herzog
Keyword(s):  
Decision Making ◽  
Political Thought ◽  
Human Beings ◽  
Starting Point ◽  
The World ◽  
Joint Decisions ◽  
Human Decision ◽  
New Beginnings

Abstract More and more decisions in our societies are made by algorithms. What are such decisions like, and how do they compare to human decision-making? I contrast central features of algorithmic decision-making with three key elements—plurality, natality, and judgment—of Hannah Arendt's political thought. In “Arendtian practices,” human beings come together as equals, exchange arguments, and make joint decisions, sometimes bringing something new into the world. With algorithmic decision-making taking over more and more areas of life, opportunities for “Arendtian practices” are under threat. Moreover, there is the danger that algorithms are tasked with decisions for which they are ill-suited. Analyzing the contrast with Arendt's thinking can be a starting point for delineating realms in which algorithmic decision-making should or should not be welcomed.

Implementation of an Intelligent Model for Decision Making Based on CBR for Supply Chain Solution in Retail for a Cluster of Supermarkets

2021 ◽  
pp. 1224-1240
Author(s):  
Adrian F. Loera-Castro ◽  
Jaime Sanchez ◽  
Jorge Restrepo ◽  
Angel Fabián Campoya Morales ◽  
Julian I. Aguilar-Duque
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Intelligent Systems ◽  
Case Based Reasoning ◽  
Human Beings ◽  
Human Decision ◽  
Decision Making Processes ◽  
A Chain ◽  
The Times ◽  
Intelligent Model

The latter includes customizing the user interface, as well as the way the system retrieves and processes cases afterward. The resulting cases may be shown to the user in different ways, and/or the retrieved cases may be adapted. This chapter is about an intelligent model for decision making based on case-based reasoning to solve the existing problem in the planning of distribution in the supply chain between a distribution center and a chain of supermarkets. First, the authors mentioned the need for intelligent systems in the decision-making processes, where they are necessary due to the limitations associated with conventional human decision-making processes. Among them, human experience is very scarce, and humans get tired of the burden of physical or mental work. In addition, human beings forget the crucial details of a problem, and many of the times are inconsistent in their daily decisions.

Fuzzy Coach-Player Systems for Controlling a Robot Manipulator

2011 ◽  
pp. 229-251
Author(s):  
Chandimal Jayawardena ◽  
Keigo Watanabe ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Natural Language ◽  
Robot Manipulator ◽  
Probabilistic Neural Network ◽  
Decision Making Process ◽  
Human Beings ◽  
Redundant Manipulator ◽  
New Concepts ◽  
Human Decision

Natural language commands are information rich and conscious because they are generated by intelligent human beings. Therefore, if it is possible to learn from such commands and reuse that knowledge, it will be very effective and useful. In this chapter, learning from information rich voice commands for controlling a robot is discussed. First, new concepts of fuzzy coach-player system and sub-coach for robot control with natural language commands are proposed. Then, the characteristics of subjective human decision making process and learning from such decisions are discussed. Finally, an experiment conducted with a PA-10 redundant manipulator in order to establish the proposed concept is described. In the experiment, a Probabilistic Neural Network (PNN) is used for learning.

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

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.

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

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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