The dynamics of deferred decision

Mutual inclusivity improves decision-making by smoothing out choice's competitive edge

Decisions form a central bottleneck to most tasks, one that people often experience as costly. Past work proposes mitigating those costs by lowering one's threshold for deciding. Here, we test an alternative solution, one that targets the basis for most choice costs: that choosing one option sacrifices others (mutual exclusivity). Across 5 studies (N = 462), we test whether this tension can be relieved by framing choices as inclusive (allowing selection of more than one option, as in buffets). We find that inclusivity makes choices more efficient, by selectively reducing competition between potential responses as participants accumulate information for each of their options. Inclusivity also made participants feel less conflicted, especially when they couldn't decide which good option to keep or which bad option to get rid of. These inclusivity benefits were also distinguishable from the effects of manipulating decision threshold (increased urgency), which improved choices but not experiences thereof.

Information sharing and deferral option in cybersecurity investment

This study investigates the effect of information sharing and deferral option on a firm's information security investment strategies by considering strategic interactions between a firm and an attacker. We find that 1) information sharing decreases a firm's security investment rate. 2) If a deferral decision is possible, the firm will decrease its immediate investment, and avoid non-investment. 3) After information sharing, the probability of a firm's deferral decision increases for low-benefit information (SL) but decreases for high-benefit information (SH). 4) When information sharing accuracy is low, a firm only defers decisions in a fraction of SL; when information sharing accuracy is high, the firm defers its decisions in all SL and a fraction of SH. 5) Information sharing can improve the effect of deferral decision when accuracy is low but weaken it when accuracy is high. These results contradict the literature, wherein information sharing reduces a firm's uncertainty on cybersecurity investment and decreases deferment options associated with investment.

Mood and implicit confidence independently fluctuate at different time scales

Mood is an important ingredient of decision-making. Human beings are immersed into a sea of ​​emotions where episodes of high mood alternate with episodes of low mood. While changes in mood are well characterized, little is known about how these fluctuations interact with metacognition, and in particular with confidence about our decisions. We evaluated how implicit measurements of confidence are related with mood states of human participants through two online longitudinal experiments involving mood self-reports and visual discrimination decision-making tasks. Implicit confidence was assessed on each session by monitoring the proportion of opt-out trials when an opt-out option was available, as well as the median reaction time on standard correct trials as a secondary proxy of confidence. We first report a strong coupling between mood, stress, food enjoyment, and quality of sleep reported by participants in the same session. Second, we confirmed that the proportion of opt-out responses as well as reaction times in non-opt-out trials provided reliable indices of confidence in each session. We introduce a normative measure of overconfidence based on the pattern of opt-out selection and the signal-detection-theory framework. Finally and crucially, we found that mood, sleep quality, food enjoyment, and stress level are not consistently coupled with these implicit confidence markers, but rather they fluctuate at different time scales: mood-related states display faster fluctuations (over one day or half-a-day) than confidence level (two-and-a-half days). Therefore, our findings suggest that spontaneous fluctuations of mood and confidence in decision making are independent in the healthy adult population.

Establishing the laws of preferential choice behavior

Mathematical and computational decision models are powerful tools for studying choice behavior, and hundreds of distinct decision models have been proposed over the long interdisciplinary history of decision making research. The existence of so many models has led to theoretical fragmentation and redundancy, obscuring key insights into choice behavior, and preventing consensus about the essential properties of preferential choice. We provide a synthesis of formal models of risky, multiattribute, and intertemporal choice, three important domains in decision making. We identify recurring insights discovered by scholars of different generations and different disciplines across these three domains, and use these insights to classify over 150 existing models as involving various combinations of eight key mathematical and computational properties. These properties capture the main avenues of theoretical development in decision making research and can be used to understand the similarities and differences between decision models, aiding both theoretical analyses and empirical tests.

Similarity, Attraction, and Compromise Effects: Original Findings, Recent Empirical Observations, and Computational Cognitive Process Models

Preference reversals—a decision maker prefers A over B in one situation but B over A in another—demonstrate that human behavior violates invariance assumptions of (utility-based) rational choice theories. In the field of multi-alternative multi-attribute decision-making research, 3 preference reversals received special attention: similarity, attraction, and compromise effects. The 3 so-called context effects are changes in (relative) choice probabilities for 2 choice alternatives after a third “decoy” option is added to the set. Despite their simplicity, the effects demonstrate that choice probabilities in multi-alternative decision making are contingent on the local context, that is, on the choice set under consideration. Because of their simplicity, on the other hand, similarity, attraction, and compromise effects have been successfully examined in numerous studies to date, and they have become of increasing interest for differentiating between computational cognitive process models of multi-alternative multi-attribute decision making. However, the stimulus arrangement for producing the effects seems to vary between studies, which becomes challenging when model accounts are compared. The purpose of this review is to present various paradigms in a coherent way and describe various model accounts based on a common structure.

Preference accumulation as a process model of desirability ratings

In desirability rating tasks, decision makers evaluate objects on a continuous response scale. Despite their prominence, full process models of these rating tasks have not been developed. We investigated whether a preference accumulation process, a process often used to model discrete choice, might explain ratings as well. According to our model, attributes from each option are sampled and evaluated stochastically. The evaluations are integrated over time, forming a preference. Preferences for options compete with each other, and accumulated preferences can decay. The model makes precise predictions regarding the statistical distribution of desirability ratings, as well as their dependence on deliberation time and on context. We test and confirm these predictions in two experimental studies. Additionally, quantitative model fits indicate that participants are better described by our proposed model, relative to a model without dynamism, competition, or stochastic attribute sampling. Our results show that the descriptive power of models of preference accumulation extends beyond discrete choice, and that the assumptions of this framework accurately characterize the core cognitive processes at play in the construction of preference and the evaluation of objects.

Similarity and decision time in preferential choice

Choice option similarity is a key contextual variable in multiattribute choice. Based on theories of preference accumulation, we predicted that decision times would be longer when the available choice options were similar compared with when they were dissimilar, controlling for the relative desirabilities of the options. We tested for the relationship between similarity and decision time in an experiment involving incentivised binary choices between items of equivalent desirability and found that our predictions were confirmed. Our results show how the effects of contextual factors on key decision variables can be accurately predicted by existing computational theories of decision-making.