scholarly journals Multi-Agent Simulation of Individuals’ Escape in the Urban Rainstorm Context Based on Dynamic Recognition-Primed Decision Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1190 ◽  
Author(s):  
Qing Yang ◽  
Xu Sun ◽  
Xingxing Liu ◽  
Jinmei Wang
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Decision Makers ◽  
Response Strategy ◽  
Limited Information ◽  
Information Gap ◽  
Agent Simulation ◽  
Multi Agent ◽  
Dynamic Recognition ◽  
Classical Decision

The urban rainstorm can evolve into a serious emergency, generally characterized by high complexity, uncertainty, and time pressure. It is often difficult for individuals to find the optimal response strategy due to limited information and time constraints. Therefore, the classical decision-making method based on the “infinite rationality” assumption is sometimes challenging to reflect the reality. Based on the recognition-primed decision (RPD) model, a dynamic RPD (D-RPD) model is proposed in this paper. The D-RPD model assumes that decision-makers can gain experience in the escaping process, and the risk perception of rainstorm disasters can be regarded as a Markov process. The experience of recent attempts would contribute more in decision-making. We design the agent according to the D-RPD model, and employ a multi-agent system (MAS) to simulate individuals’ decisions in the context of a rainstorm. Our results show that experience helps individuals to perform better when they escape in the rainstorm. Recency acts as a one of the key elements in escaping decision making. We also find that filling the information gap between individuals and real-time disaster would help individuals to perform well, especially when individuals tend to avoid extreme decisions.

scholarly journals Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy

2016 ◽  
Vol 113 (31) ◽  
pp. E4531-E4540 ◽  
Author(s):  
Braden A. Purcell ◽  
Roozbeh Kiani
Keyword(s):  
Decision Making ◽  
Neural Mechanism ◽  
Decision Processes ◽  
Response Strategy ◽  
Limited Information ◽  
Decision Strategy ◽  
Stimulus Response ◽  
Hierarchical Decision ◽  
The Right ◽  
High Level

Decision-making in a natural environment depends on a hierarchy of interacting decision processes. A high-level strategy guides ongoing choices, and the outcomes of those choices determine whether or not the strategy should change. When the right decision strategy is uncertain, as in most natural settings, feedback becomes ambiguous because negative outcomes may be due to limited information or bad strategy. Disambiguating the cause of feedback requires active inference and is key to updating the strategy. We hypothesize that the expected accuracy of a choice plays a crucial rule in this inference, and setting the strategy depends on integration of outcome and expectations across choices. We test this hypothesis with a task in which subjects report the net direction of random dot kinematograms with varying difficulty while the correct stimulus−response association undergoes invisible and unpredictable switches every few trials. We show that subjects treat negative feedback as evidence for a switch but weigh it with their expected accuracy. Subjects accumulate switch evidence (in units of log-likelihood ratio) across trials and update their response strategy when accumulated evidence reaches a bound. A computational framework based on these principles quantitatively explains all aspects of the behavior, providing a plausible neural mechanism for the implementation of hierarchical multiscale decision processes. We suggest that a similar neural computation—bounded accumulation of evidence—underlies both the choice and switches in the strategy that govern the choice, and that expected accuracy of a choice represents a key link between the levels of the decision-making hierarchy.

Technology Imperative in Managerial Decision-Making

2014 ◽  
pp. 158-176 ◽  
Author(s):  
Neeta Baporikar
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Limited Information ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Face To Face ◽  
The Core ◽  
Use Of Technology ◽  
Management Approaches ◽  
Virtual Setting

Decisions can make or mar an organization. Decision-making is a multifaceted and intricate process. This process becomes even more complicated and complex when it comes to organizations, especially in this competitive world. Today, decisions are made not only under uncertainty, with available and/or limited information, but may also be made in a virtual setting. Decision makers may not be engaged in face-to-face deliberations. Hence, understanding the challenges, complexity, and rewards of the use of technology, especially information technology in managerial decision-making, is important. Such an understanding is not only vital in determining the efficacy of managers and their organizations, but also significant in designing future management approaches and organizations. This is the core objective of this chapter.

Transdisciplinary Approach to Service Design Based on Consumer’s Value and Decision Making

2012 ◽  
pp. 57-74
Author(s):  
Takeshi Takenaka ◽  
Kousuke Fujita ◽  
Nariaki Nishino ◽  
Tsukasa Ishigaki ◽  
Yoichi Motomura
Keyword(s):  
Decision Making ◽  
Cognitive Model ◽  
Service Design ◽  
Research Area ◽  
Service Industries ◽  
Agent Based ◽  
Movie Theaters ◽  
Retail Business ◽  
Agent Simulation ◽  
Multi Agent

Science and technology are expected to support actual service provision and to create new services to promote service industries’ productivity. However, those problems might not be solved solely in a certain research area. This paper describes that it is necessary to establish transdisciplinary approaches to service design in consideration of consumers’ values and decision making. Recent research trends of services are overviewed. Then a research framework is proposed to integrate computer sciences, human sciences, and economic sciences. Three study examples of services are then presented. The first study is a multi-agent simulation of a cellular telephone market based on results of a psychological survey. The second presents a cognitive model constructed through integration of questionnaire data of a retail business and Bayesian network modeling. The third presents a pricing mechanism design for service facilities––movie theaters––using an economic experiment and agent-based simulation.

Simple Strategies or Simple Tasks? Dynamic Decision Making in “Complex” Worlds

1996 ◽  
Vol 40 (4) ◽  
pp. 184-188
Author(s):  
Alex Kirlik ◽  
Ling Rothrock ◽  
Neff Walker ◽  
Arthur D. Fisk
Keyword(s):  
Decision Making ◽  
Empirical Evidence ◽  
Time Pressure ◽  
Task Complexity ◽  
Empirical Finding ◽  
Decision Makers ◽  
Dynamic Decision Making ◽  
Decision Problems ◽  
Decision Strategies

Decision makers in operational environments perform in a world of dynamism, time pressure, and uncertainty. Perhaps the most stable empirical finding to emerge from naturalistic studies in these domains is that, despite apparent task complexity, performers only rarely report the use of complex, enumerative decision strategies. If we accept that decision making in these domains is often effective, we are presented with a dilemma: either decision strategies are (covertly) more complex than these performers claim, or these tasks are (subtlely) more simple than they might appear. We present a set of empirical findings and modeling results which suggest the latter explanation: that the simplicity of decision making is not merely apparent but largely real, and that tasks of high apparent complexity may yet admit to rather simple types of decision strategies. We also discuss empirical evidence that sheds light on the error forms resulting from the tendency of performers to seek and employ heuristic solutions to dynamic, uncertain decision problems.

Transdisciplinary Approach to Service Design Based on Consumer's Value and Decision Making

2010 ◽  
Vol 1 (1) ◽  
pp. 58-75 ◽  
Author(s):  
Takeshi Takenaka ◽  
Kousuke Fujita ◽  
Nariaki Nishino ◽  
Tsukasa Ishigaki ◽  
Yoichi Motomura
Keyword(s):  
Decision Making ◽  
Cognitive Model ◽  
Service Design ◽  
Research Area ◽  
Service Industries ◽  
Agent Based ◽  
Movie Theaters ◽  
Retail Business ◽  
Agent Simulation ◽  
Multi Agent

Science and technology are expected to support actual service provision and to create new services to promote service industries’ productivity. However, those problems might not be solved solely in a certain research area. This paper describes that it is necessary to establish transdisciplinary approaches to service design in consideration of consumers’ values and decision making. Recent research trends of services are overviewed. Then a research framework is proposed to integrate computer sciences, human sciences, and economic sciences. Three study examples of services are then presented. The first study is a multi-agent simulation of a cellular telephone market based on results of a psychological survey. The second presents a cognitive model constructed through integration of questionnaire data of a retail business and Bayesian network modeling. The third presents a pricing mechanism design for service facilities––movie theaters––using an economic experiment and agent-based simulation.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2011 ◽  
pp. 35-59
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2009 ◽  
pp. 281-300
Author(s):  
Andrew P. Sage ◽  
Cynthia T. Small
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This chapter describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid Knowledge Management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

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