Decision Processes and Decision-Making in Changeable Spaces

2016 ◽  
pp. 25-50
Author(s):  
Moussa Larbani ◽  
Po-Lung Yu
Keyword(s):  
Decision Making ◽  
Decision Processes

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.

Markov decision processes for integrating life cycle dynamics into fab-level decision making

1999 ◽  
Vol 32 (2) ◽  
pp. 4852-4857
Author(s):  
Shalabh Bhatnagar ◽  
Michael C. Fu ◽  
Steven I. Marcus ◽  
Ying He
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Markov Decision

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Process and Content in Decisions from Memory

2019 ◽  
Author(s):  
Wenjia Joyce Zhao ◽  
Russell Richie ◽  
Sudeep Bhatia
Keyword(s):  
Decision Making ◽  
Ethical Decision Making ◽  
Memory Search ◽  
Well Being ◽  
Quantitative Model ◽  
Decision Processes ◽  
Language Models ◽  
Choice Data ◽  
Model Driven ◽  
Decision Context

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this paper we present a general framework that is capable of addressing the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought-listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to study risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and judgments about well-being, society and culture.

scholarly journals Learning While Deciding in Groups

2019 ◽  
Author(s):  
Scott Tindale ◽  
Jeremy R. Winget
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Processes ◽  
Learning Potential ◽  
Group Members ◽  
Actual Learning

Groups are used to make many important societal decisions. Similar to individuals, by paying attention to the information available during the decision processes and the consequences of the decisions, groups can learn from their decisions as well. In addition, group members can learn from each other by exchanging information and being exposed to different perspectives. However, groups make decisions in many different ways and the potential and actual learning that takes place will vary as a function of the manner in which groups reach consensus. This chapter reviews the literature on group decision making with a special emphasis on how and when group decision making leads to learning. We argue that learning is possible in virtually any group decision making environment but freely interacting groups create the greatest potential for learning. We also discuss when and why group may not always take advantage of the learning potential.

scholarly journals Informational Openness Enhances Decentralized Decision-Making: A Cognitive Agent Based Study

2017 ◽  
Author(s):  
Joshua Skewes ◽  
Dorthe Døjbak Håkonsson ◽  
Trine Bilde ◽  
Andreas Roepstorff
Keyword(s):  
Decision Making ◽  
Group Performance ◽  
Decision Processes ◽  
Abstract Entity ◽  
Collaborative Decision Making ◽  
Distributed Information ◽  
Decentralized Decision Making ◽  
Or Organization ◽  
Gains And Losses ◽  
Collaborative Decision

Collaborative decision making is central to the organization of society. Juries deliberate cases, voters elect government officials, open innovation networks converge on innovative solutions. It is common to think of such groups as decision making entities. But this language is imprecise. Real decision processes do not occur within any group or organization as an abstract entity. Collaborative decision making happens within and between autonomous individuals. This emphasizes the importance of the relationships between individual and social decision-making processes to social organization. Despite a rich body of literature on collaborative decision making we know little about how individuals decide to commit to group decision making in the first place, and how, once joined, they communicate their distributed information for optimal group performance. We introduce a general framework designed to model collaborative decision processes. Our main results are that 1) commitment and gain is enhanced when groups are designed so agents have realistic knowledge about the forgone gains and losses associated with abstaining from the group; and 2) that this effect is accelerated when communication between group members conveys more information about individual preferences. We thus demonstrate that collaborative decision making is done best when it is done by groups that are informationally open.

Explaining monotonic ranking functions

2020 ◽  
Vol 14 (4) ◽  
pp. 640-652
Author(s):  
Abraham Gale ◽  
Amélie Marian
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
A Priori ◽  
Data Distribution ◽  
Synthetic Data ◽  
Decision Processes ◽  
Decision Makers ◽  
Ranking Functions ◽  
High School Admissions ◽  
Parameter Values

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

Examining the Implications of Process and Choice for Strategic Decision Making Effectiveness

2013 ◽  
pp. 344-359
Author(s):  
Paul L. Drnevich ◽  
Thomas H. Brush ◽  
Alok Chaturvedi
Keyword(s):  
Decision Making ◽  
Academic Research ◽  
Decision Processes ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Political Decision ◽  
Agent Based ◽  
Response Choice ◽  
Decision Making Processes ◽  
Using Data

Most strategic decision-making (SDM) approaches advocate the importance of decision-making processes and response choices for obtaining effective outcomes. Modern decision-making support system (DMSS) technology is often also needed for complex SDM, with recent research calling for more integrative DMSS approaches. However, scholars tend to take disintegrated approaches and disagree on whether rational or political decision-making processes result in more effective decision outcomes. In this study, the authors examine these issues by first exploring some of the competing theoretical arguments for the process-choice-effectiveness relationship, and then test these relationships empirically using data from a crisis response training exercise using an intelligent agent-based DMSS. In contrast to prior research, findings indicate that rational decision processes are not effective in crisis contexts, and that political decision processes may negatively influence both response choice and decision effectiveness. These results offer empirical evidence to confirm prior unsupported arguments that response choice is an important mediating factor between the decision-making process and its effectiveness. The authors conclude with a discussion of the implications of these findings and the application of agent-based simulation DMSS technologies for academic research and practice.

Ethical and Regulatory Challenges of Emerging Health Technologies

2022 ◽  
pp. 84-100
Author(s):  
Samia Hassan Rizk
Keyword(s):  
Decision Making ◽  
Patient Safety ◽  
Data Privacy ◽  
Health Technology ◽  
Decision Processes ◽  
The Other ◽  
Health Technologies ◽  
Regulatory Decision ◽  
Data Analytic ◽  
Human Healthcare

The advances in biotechnology and computer and data sciences opened the way for innovative approaches to human healthcare. Meanwhile, they created many ethical and regulatory dilemmas such as pervasive global inequalities and security and risk to data privacy. The assessment of health technology is a systematic multidisciplinary process that aims to examine the benefits and risks associated with its use including medical, social, economic, and ethical impacts. It is used to inform policy and optimize decision-making. The advance of technology is creating significant challenges to healthcare regulators who strive to balance patient safety to fostering innovation. The FDA and EMA are modernizing their regulatory approaches to foster innovation in digital technology and improve safety and applicability to patients. On the other hand, data analytic technologies have been introduced into regulatory decision processes.

IT project decisions: conclusions and recommendations for corporate practice

2019 ◽  
Vol 12 (4) ◽  
pp. 1030-1060
Author(s):  
Boris David Idler ◽  
Konrad Spang
Keyword(s):  
Decision Making ◽  
Portfolio Management ◽  
Qualitative Content Analysis ◽  
Decision Processes ◽  
Content Type ◽  
Decision Variables ◽  
It Project ◽  
Industry Comparison ◽  
Corporate Practice ◽  
Project Decision

Purpose The purpose of this paper is to clarify the relevant determinants of IT project decision making and their relevance in corporate practice. Design/methodology/approach The empiric analysis used in-depth expert interviews (n=18) as method for data collection and qualitative content analysis using evaluative categories for analysis. Findings Corporate practice is strongly influenced by descriptive decision making. There is only little use of normative decision models in decision making. In corporate practice little use is made of evaluations to analyze achieved project outputs and impacts to improve decision-making practice. This is the result of several evaluation barriers in organizations. Research limitations/implications The sample is restricted to IT projects as the experts are responsible for IT project portfolio management. Also, an industry comparison is not included in the study. Practical implications The analysis shows that IT project decision making in corporate practice should include results from descriptive decision theory into project decision processes in corporate practice. More effort should be made in challenging project input data which is relevant for project decision making. By systematically including evaluations for relevant projects, the deviations between planned and achieved project impacts offer valuable feedback for estimators and decision makers. Originality/value The paper presents detailed analysis on decision variables and their relevance for IT project decision making in corporate practice. Critical aspects of decision making become clear, such as the aspects of evaluation barriers and the need to incorporate descriptive decision-making aspects into corporate decision processes.

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