EXPRESS: Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms versus Humans

2021 ◽  
pp. 002224372110700
Author(s):  
Gizem Yalcin ◽  
Sarah Lim ◽  
Stefano Puntoni ◽  
Stijn M. J. van Osselaer
Keyword(s):  
Decision Maker ◽  
Consumer Reactions ◽  
Human Decision

Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers’ reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers’ predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: It is easier for consumers to internalize a favorable decision outcome that is rendered by a human (vs. an algorithm), while it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

scholarly journals Fixation patterns in simple choice reflect optimal information sampling

2019 ◽  
Author(s):  
Frederick Callaway ◽  
Antonio Rangel ◽  
Tom Griffiths
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Sequential Sampling ◽  
Empirical Work ◽  
Reaction Times ◽  
Optimal Solution ◽  
Cognitive Resources ◽  
Human Decision ◽  
Information Sampling ◽  
Optimal Information

When faced with a decision between several options, people rarely fully consider every alternative. Instead, we direct our attention to the most promising candidates, focusing our limited cognitive resources on evaluating the options that we are most likely to choose. A growing body of empirical work has shown that attention plays an important role in human decision making, but it is still unclear how people choose with option to attend to at each moment in the decision making process. In this paper, we present an analysis of how a rational decision maker should allocate her attention. We cast attention allocation in decision making as a sequential sampling problem, in which the decision maker iteratively selects from which distribution to sample in order to update her beliefs about the values of the available alternatives. By approximating the optimal solution to this problem, we derive a model in which both the selection and integration of evidence are rational. This model predicts choices and reaction times, as well as sequences of visual fixations. Applying the model to a ternary-choice dataset, we find that its predictions align well with human data.

scholarly journals Stochastic representation decision theory: How probabilities and values are entangled dual characteristics in cognitive processes

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243661
Author(s):  
Giuseppe M. Ferro ◽  
Didier Sornette
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Time Pressure ◽  
Rational Expectation ◽  
Weighting Function ◽  
Objective Probability ◽  
Decision Making Process ◽  
Stochastic Representation ◽  
Human Decision ◽  
Expectation Theory

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process. It predicts the famous fourfold pattern of risk preferences. Through the analysis of choice probabilities, it is possible to identify two previously postulated features of prospect theory: the inverse S-shaped subjective probability as a function of the objective probability and risk-seeking behavior in the loss domain. It also predicts observed violations of stochastic dominance, while it does not when the dominance is “evident”. Extending the model to account for human finite deliberation time and the effect of time pressure on choice, it provides other sound predictions: inverse relation between choice probability and response time, preference reversal with time pressure, and an inverse double-S-shaped probability weighting function. Our theory, which offers many more predictions for future tests, has strong implications for psychology, economics and artificial intelligence.

The President and the Political Use of Force

1986 ◽  
Vol 80 (2) ◽  
pp. 541-566 ◽  
Author(s):  
Charles W. Ostrom ◽  
Brian L. Job
Keyword(s):  
World War Ii ◽  
Decision Maker ◽  
Decision Rule ◽  
Use Of Force ◽  
The Political ◽  
World War ◽  
Human Decision ◽  
Simple Decision Rule ◽  
Presidential Decisions

Throughout the post–World War II period the president has been called upon to make decisions concerning the use of force as a political instrument. The explanation that is offered is based upon a characterization of the president as a cybernetic human decision maker facing limitations. These limitations, in conjunction with the complexity of the environment, lead presidents to develop and use a relatively simple decision rule. The dependent variable, which is the probability of the use of force at any point in time, is explained in terms of enduring and essential concerns, which are operationalized as coming from the international, domestic, and personal environments. Data are taken from Blechman and Kaplan's Force Without War. On the basis of our estimation and evaluation, presidential decisions to use force are based on factors in all three arenas.

scholarly journals Towards Interactive Evolution: A Distributed Optimiser for Multi-Objective Water Distribution Network Design

10.29007/ltkb ◽  
2018 ◽  
Author(s):  
David Walker ◽  
Matthew Johns ◽  
Ed Keedwell ◽  
Dragan Savic
Keyword(s):  
Evolutionary Algorithms ◽  
Network Design ◽  
Decision Maker ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Benchmark Problems ◽  
Optimal Solutions ◽  
Human Decision ◽  
Water Distribution Network Design

It is well known that water distribution networks can be optimised by evolutionary algorithms. However, while such optimisation can result in mathematically optimal solutions, the ability of the algorithm to generate novelty can often lead to solutions that are not practical for implementation. This work describes a distributed optimisation platform that will enable the inclusion of a human decision maker in the optimisation process. The architecture of the platform is described, and examples of its execution on benchmark problems is described, using an automated client that interacts with the platform in lieu of a human decision maker.

scholarly journals A Model for Accountable Ordinal Sorting

2017 ◽  
Author(s):  
Khaled Belahcene ◽  
Christophe Labreuche ◽  
Nicolas Maudet ◽  
Vincent Mousseau ◽  
Wassila Ouerdane
Keyword(s):  
Decision Maker ◽  
Recommender System ◽  
Human Decision ◽  
Model Features

We address the problem of multicriteria ordinalsorting through the lens of accountability, i.e. theability of a human decision-maker to own a recommendationmade by the system. We put forward anumber of model features that would favor the capabilityto support the recommendation with a convincingexplanation. To account for that, we designa recommender system implementing and formalizingsuch features. This system outputs explanationsdefined under the form of specific argumentschemes tailored to represent the specific rules ofthe model. At the end, we discuss possible andpromising argumentative perspectives.

The Mythical Decision Maker

2011 ◽  
pp. 653-660 ◽  
Author(s):  
Csaba Csáki
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Maker ◽  
Support Systems ◽  
Historical Context ◽  
Decision Makers ◽  
Organizational Decision Making ◽  
Theoretical Investigations ◽  
Human Decision ◽  
History Of

During the history of decision support systems (DSSs)— in fact, during the history of theoretical investigations of human decision-making situations—the decision maker (DM) has been the centre of attention who considers options and makes a choice. However, the notion and definitions of this decision maker, as well as the various roles surrounding his or her activity, have changed depending on both time and scientific areas. Reading the DSS literature, one might encounter references to such players as decision makers, problem owners, stakeholders, facilitators, developers, users, project champions, and supporters, and the list goes on. Who are these players, what is their role, and where do these terms come from? This article presents a review in historical context of some key interpretations aimed at identifying the various roles that actors may assume in an organizational decision-making situation.

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