Chance Discovery as Analogy Based Value Sensing

The authors are finding rising demands for sensing values in existing/new events and items in the real life. Chance discovery, focusing on new events significant for human decision making, can be positioned extensively as an approach to value sensing. This extension enables the innovation of various artificial systems, where human’s talent of analogical thinking comes to be the basic engine. Games for training and activating this talent are introduced, and it is clarified that these games train the an essential talent of human for chance discovery, by discussing the experimental results of these games on the logical framework of analogical abductive reasoning.

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Chance Discovery as Analogy Based Value Sensing

Intelligent and Knowledge-Based Computing for Business and Organizational Advancements ◽

10.4018/978-1-4666-1577-9.ch003 ◽

2012 ◽

pp. 43-56

Author(s):

Yukio Ohsawa ◽

Akinori Abe ◽

Jun Nakamura

Keyword(s):

Decision Making ◽

Real Life ◽

Abductive Reasoning ◽

Experimental Results ◽

Logical Framework ◽

Chance Discovery ◽

Analogical Thinking ◽

Life Chance ◽

Basic Engine ◽

Human Decision

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Selective outcome reinstatement during evaluation drives heuristics in risky choice

10.31234/osf.io/kb6ew ◽

2021 ◽

Author(s):

Evan Russek ◽

Rani Moran ◽

Yunzhe Liu ◽

Raymond J Dolan ◽

Quentin JM Huys

Keyword(s):

Decision Making ◽

Risk Taking ◽

Real Life ◽

Risky Choice ◽

Behavioral Models ◽

Behavioral Differences ◽

Actual Choice ◽

Human Decision ◽

Probability Information ◽

Reward Information

A ubiquitous feature of human decision making under risk is that individuals differ from each other, as well as from normativity, in how they incorporate reward and probability information. One possible explanation for these deviations is a desire to reduce the number of potential outcomes considered during choice evaluation. Although multiple behavioral models can be invoked involving selective consideration of choice outcomes, whether differences in these tendencies underlie behavioral differences in sensitivity to reward and probability information is unknown. Here we consider neural evidence where we exploit magnetoencephalography (MEG) to decode the actual choice outcomes participants consider when they decide between a gamble and a safe outcome. We show that variability in tendencies of individual participants to reinstate neural outcome representations, based on either their probability or reward, explains variability in the extent to which their choices reflect consideration of probability and reward information. In keeping with this we also show that participants who are higher in behavioral impulsivity fail to preferentially reinstate outcomes with higher probability. Our results suggest that neural differences in the degree to which outcomes are considered shape risk taking strategy, both in decision making tasks, as well as in real life.

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Choose your own risks: measuring risk-taking through an interactive novel

10.31234/osf.io/w2pzu ◽

2019 ◽

Author(s):

Alexandre Bran ◽

David C. F. Vaidis

Keyword(s):

Decision Making ◽

Risk Taking ◽

Real Life ◽

Self Report ◽

The Novel ◽

Strong Correlations ◽

Human Decision ◽

Made In

We present elements that support the use of interactive novels as a relevant risk-taking measure instrument. In the introduction, we describe what interactive novels are and how they can be used to study human decision making. Then, we present the results of a study designed to assess how behaviours made in interactive novels relate to real-life behaviours. Participants (n = 234) interacted with a novel depicting a student party. During the story, participants could make several risk-taking behaviours (i.e., drug taking). Then, participants completed the Dohmen’s one item risk-taking scale, the DOSPERT scale, and a self-report risk-taking scale. Our results show strong correlations between risk-taking in the interactive novel and real life risk-taking behaviours. Notably, risk-taking behaviours made in the novel were globally more correlated with real-life risk-taking than were the Dohmen and DOSPERT scales. This supports the use of interactive novels as an instrument to study risk-taking behaviours and decision making.

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Primacy, Congruence and Confidence in Diagnostic Decision-Making

10.31234/osf.io/f9382 ◽

2020 ◽

Author(s):

Thomas F. Frotvedt ◽

Øystein Bondevik ◽

Vanessa T. Seeligmann ◽

Bjørn Sætrevik

Keyword(s):

Decision Making ◽

Real Life ◽

Primacy Effect ◽

Symptom Presentation ◽

Human Decision ◽

Decision Making Processes ◽

Complex Decision Making ◽

Disconfirming Information ◽

Diagnostic Decision Making

Some heuristics and biases are assumed to be universal for human decision-making, and may thus be expected to appear consistently and need to be considered when planning for real-life decision-making. Yet results are mixed when exploring the biases in applied settings, and few studies have attempted to robustly measure the combined impact of various biases during a decision-making process. We performed three pre-registered classroom experiments in which trained medical students read case descriptions and explored follow-up information in order to reach and adjust mental health diagnoses (∑N = 224). We tested whether the order of presenting the symptoms led to a primacy effect, whether there was a congruence bias in selecting follow-up questions, and whether confidence increased during the decision process. Our results showed increased confidence for participants that did not change their decision or sought disconfirming information. There was some indication of a weak congruence bias in selecting follow-up questions. There was no indication of a stronger congruence bias when confidence was high, and there was no support for a primacy effect of the order of symptom presentation. We conclude that the biases are difficult to demonstrate in pre-registered analyses of complex decision-making processes in applied settings.

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Decision Making in Dynamic Environments: Fixation Errors and Their Causes

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129503900906 ◽

1995 ◽

Vol 39 (9) ◽

pp. 469-473 ◽

Cited By ~ 14

Author(s):

Y. Xiao ◽

C. F. Mackenzie ◽

Lotas Group

Keyword(s):

Decision Making ◽

Correct Diagnosis ◽

Real Life ◽

Dynamic Environments ◽

Work Environments ◽

System Stability ◽

Unique Type ◽

Two Factors ◽

False Sense ◽

Human Decision

One of the goals of naturalistic studies of human decision making is to reveal the cognitive loads or task difficulties imposed on the decision maker in real work environments. Fixation errors or cognitive lockups have been reported as a unique type of performance failure in dynamic work environments, and are thus particularly valuable to the understanding of the challenges and difficulties confronting practitioners in dynamic environments. In this paper, we present the analysis of fixation errors during real-life trauma patient resuscitation. The analysis elicits two factors, both rooted in the inherent complexity of the domain, that contributed to the occurrence of fixation errors: unreliable monitoring devices and delayed feedback. The former induces the behavior of preferring confirmatory information, partly for redundancy checks. The latter may create a false sense of system stability and divert attention away from the correct diagnosis.

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Decision policy scenarios for just-in-sequence (JIS) deliveries

Journal of Industrial Engineering and Management ◽

10.3926/jiem.2090 ◽

2017 ◽

Vol 10 (4) ◽

pp. 581 ◽

Cited By ~ 5

Author(s):

Miguel Gaston Cedillo-Campos ◽

Dario Morones Ruelas ◽

Giovanni Lizarraga-Lizarraga ◽

Jesus Gonzalez-Feliu ◽

Jose Arturo Garza-Reyes

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Manufacturing Industry ◽

Real Life ◽

Abductive Reasoning ◽

Periodic Review ◽

Manufacturing Companies ◽

Scholarly Research ◽

Policy Scenarios

Purpose: The Just-in-Sequence (JIS) approach is evidencing advantages in efficiently managing variety-driven costs, and reducing the risk of disruption in sourcing, manufacturing companies and third-party logistics. This has increased its implementation in the manufacturing industry, especially in highly customized manufacturing sectors such as the automotive industry. However, despite its growing interest by manufacturers, scholarly research focused on JIS still remains limited. In this context, little has been done to study the effect of JIS on the fluidity of supply chains and processes of logistics suppliers as well as providing them with a decision making tool to optimise the sequencing of their deliveries. Therefore, the aim of this paper is to propose a genetic algorithm to evaluate different decision policy scenarios to reduce risks of supply disruptions at the final assembly line. Consequently, an algorithm considering a periodic review of the inventory that assumes a steady demand and short response times is developed and applied.Design/methodology/approach: Based on a literature review and real-life information, an abductive reasoning was performed and a case study application of the proposed algorithm conducted in the auto-industry.Findings: The results obtained from the case study indicate that the proposed genetic algorithm offers a reliable solution when facing variability in safety stocks that operate under assumptions such as: i) fixed costs; ii) high inventory turnover; iii) scarce previous information available concerning material requirements; and iv) replenishment services as core business value. Although the results are based on an auto-industry case study, they are equally applicable to other global supply chains.Originality/value: This paper is of interest to practitioners and academics alike as it complements and supports the very limited scholarly research on JIS by providing manufacturers and 3PL suppliers competing in mass customized industries and markets a tool to support decision-making. Implications for the design of modern supply chain fluidity in the manufacturing industry are also exposed and future research streams presented.

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Optimal policy for attention-modulated decisions explains human fixation behavior

10.1101/2020.08.04.237057 ◽

2020 ◽

Cited By ~ 1

Author(s):

Anthony Jang ◽

Ravi Sharma ◽

Jan Drugowitsch

Keyword(s):

Decision Making ◽

Real Life ◽

Relevant Information ◽

Decision Makers ◽

Cognitive Resources ◽

Decision Performance ◽

Human Decision ◽

Behavioral Predictions ◽

Decision Making Model ◽

Fixation Bias

AbstractTraditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information [46, 23, 21, 12, 15]. These fixations also causally affect one’s choices, biasing them toward the longer-fixated item [38, 2, 25]. We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models [25, 39], we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings [3, 13, 29, 45]. Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources.

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Neural precursors of decisions that matter—an ERP study of deliberate and arbitrary choice

eLife ◽

10.7554/elife.39787 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 6

Author(s):

Uri Maoz ◽

Gideon Yaffe ◽

Christof Koch ◽

Liad Mudrik

Keyword(s):

Decision Making ◽

Real Life ◽

Neural Mechanisms ◽

Causal Role ◽

Editorial Note ◽

Drift Diffusion Model ◽

Non Profit ◽

Human Decision ◽

Arbitrary Decision ◽

Random Fluctuations

The readiness potential (RP)—a key ERP correlate of upcoming action—is known to precede subjects' reports of their decision to move. Some view this as evidence against a causal role for consciousness in human decision-making and thus against free-will. But previous work focused on arbitrary decisions—purposeless, unreasoned, and without consequences. It remains unknown to what degree the RP generalizes to deliberate, more ecological decisions. We directly compared deliberate and arbitrary decision-making during a $1000-donation task to non-profit organizations. While we found the expected RPs for arbitrary decisions, they were strikingly absent for deliberate ones. Our results and drift-diffusion model are congruent with the RP representing accumulation of noisy, random fluctuations that drive arbitrary—but not deliberate—decisions. They further point to different neural mechanisms underlying deliberate and arbitrary decisions, challenging the generalizability of studies that argue for no causal role for consciousness in decision-making to real-life decisions.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

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