One-shot Decision Research Articles

Of two minds: an experiment on how time scarcity shapes risk-taking behaviour

ABSTRACT Several studies report that the brain evaluates prospects and executes decisions as the outcome of two types of mental processes: one described as slow and reflective and the other as fast and intuitive. We investigate how these two mental processes affect risk-taking behaviour in a one-shot decision by using time pressure to establish an intuitive response. We observe that time constraints do not change risk attitudes. Furthermore, it is only when subjects are given ample time to decide and instructed to reflect that they show the well-documented shift of risk preferences across the domain of losses and gains. Our results suggest that framing effects are the result of cognitive processes that become operative when individuals are granted sufficient time to undertake a more comprehensive analysis of decision tasks.

Why Should We Study the Foreign Language Effect: Debiasing through Affecting Metacognition?

Debiasing is a method of improving people's decisions by reducing their reliance on salient intuitions causing them to behave suboptimally or biasedly. However, many of the known debiasing techniques have limited effectiveness or can only remedy a one-shot decision, rather than having a lasting impact. In this work, I focus on the role of metacognition in debiasing decision-making and discuss how it can be better understood through the lens of the foreign language effect. The foreign language effect suggests that using a foreign language can sometimes benefit people's decision-making without providing them with additional information or instructions regarding the task. However, we do not fully understand how the foreign language effect works and its limitations. I conclude by urging scientists to research this effect with the hope of having a lasting positive impact on society.

The Tree Task: An Incentivized, One-shot Decision Task to Measure Pro-environmental Behavior

The Tree Task: An Incentivized, One-shot Decision Task to Measure Pro-environmental Behavior

Fair and Explainable Depression Detection in Social Media

Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner.

Optimal adaptive inspection and maintenance for redundant systems

Optimal exploration of engineering systems can be guided by the principle of Value of Information (VoI), which accounts for the topological important of components, their reliability and the management costs. For series systems, in most cases higher inspection priority should be given to unreliable components. For redundant systems such as parallel systems, analysis of one-shot decision problems shows that higher inspection priority should be given to more reliable components. This paper investigates the optimal exploration of redundant systems in long-term decision making with sequential inspection and repairing. When the expected, cumulated, discounted cost is considered, it may become more efficient to give higher inspection priority to less reliable components, in order to preserve system redundancy. To investigate this problem, we develop a Partially Observable Markov Decision Process (POMDP) framework for sequential inspection and maintenance of redundant systems, where the VoI analysis is embedded in the optimal selection of exploratory actions. We investigate the use of alternative approximate POMDP solvers for parallel and more general systems, compare their computation complexities and performance, and show how the inspection priorities depend on the economic discount factor, the degradation rate, the inspection precision, and the repair cost.

Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games

Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player faces in an extensive-form game, as well as Markov decision processes and partially-observable Markov decision processes where the agent conditions on observed history. Over the past decade, there has been considerable effort into designing online optimization methods for TFSDM. Virtually all of that work has been in the full-feedback setting, where the agent has access to counterfactuals, that is, information on what would have happened had the agent chosen a different action at any decision node. Little is known about the bandit setting, where that assumption is reversed (no counterfactual information is available), despite this latter setting being well understood for almost 20 years in one-shot decision making. In this paper, we give the first algorithm for the bandit linear optimization problem for TFSDM that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O(sqrt(T)) cumulative regret in expectation compared to any fixed strategy, at all times T. This is made possible by new results that we derive, which may have independent uses as well: 1) geometry of the dilated entropy regularizer, 2) autocorrelation matrix of the natural sampling scheme for sequence-form strategies, 3) construction of an unbiased estimator for linear losses for sequence-form strategies, and 4) a refined regret analysis for mirror descent when using the dilated entropy regularizer.

Honesty pledges for the behaviorally-based regulation of dishonesty

ABSTRACT A common regulatory dilemma is determining how much trust authorities can place in people’s self-reports, especially in contexts with an incentive to cheat. In such contexts, regulators are typically risk averse and do not readily confer trust, resulting in excessive requirements when applying for permits, licenses, etc. Studies in behavioral ethics have suggested that asking people to ex-ante pledge to behave ethically can reduce their level of dishonesty and noncompliance. However, pledges might also backfire by allowing more people to cheat with no real sanctions. Additionally, pledges’ effects have almost always been studied in one-shot decision making without sanctions. We explore pledges’ potential effects by manipulating whether pledges were accompanied by sanctions (fines) and testing their impact on sequential, repeated ethical decisions. We find that pledges can considerably and consistently reduce dishonesty, and this effect is not crowded out by the presence of fines. Furthermore, pledges also affect “brazen liars” who cheat to a large extent and also those who score low on tendency to follow rules and norms. We conclude that pledges could be an effective tool for the behavioral regulation of dishonesty, to reduce regulatory burden, and to build a more trusting relationship between government and the public.

Personality information sharing in supply chain systems for innovative products in the circular economy era

This research explores the value of personality information sharing in a two-tier supply chain of innovative products that have a short life cycle and no pollution to the environment. We analyse the behaviour of supply chain participants with one-shot decision theory and present an analysis of wholesale pricing for these products. We introduce the retailer's personality information sharing into our models and show the importance of personality information sharing in the wholesale price contract of the supply chain system. Theoretical analysis gains managerial insights into the strategic selection of the manufacturer when facing retailers with different personalities.

Honesty Pledges for the Behaviorally-based Regulation of Dishonesty

A common regulatory dilemma is determining how much trust authorities can place in people’s self-reports, especially in contexts where the incentive to cheat is high. In such contexts, regulators are typically risk averse and do not readily confer trust, resulting worldwide in excessive requirements when applying for permits, licenses, and the like. Studies in behavioral ethics have suggested that asking people to ex-ante pledge to behave ethically can reduce their level of dishonesty and noncompliance. However, pledges might also backfire by allowing more people to cheat with no real sanctions. Additionally, pledges’ effects have almost always been studied in one-shot decision making without sanctions. We explore pledges’ potential effects by manipulating whether pledges were accompanied by sanctions (fines) and testing their impact on sequential, repeated ethical decisions. We find that pledges can considerably and consistently reduced dishonesty, and this effect is not crowded out by the presence of fines. Furthermore, pledges also affect “brazen liars” who cheat to a large extent and also those who score low on tendencies to follow rules and norms. We conclude that pledges could be an effective tool for the behavioral regulation of dishonesty, reduce the regulatory burden, and build a more trusting relationship between government and the public.

The impact of variability and prechoice experience on taking safety measures: The case of security updates

AbstractThe goal of this research is to clarify the conditions that trigger reluctance to take cost‐effective safety measures. We present three experiments. In two of the experiments, the participants were asked to operate a simulated system for 20 periods, each with 10 trials. They could “update the system” to eliminate the risk of a “security failure” that led to a loss of 100 points. The updating cost was either fixed (at 10 points) or variable (initially 10 points, and some probability of free—0 points—updates). The optimal strategy prescribed updating at the first opportunity. Another experiment focused on one‐shot decision under risk. The results highlight two factors that reduce the tendency to update and impair performance: cost variability and prechoice experience. Importantly, we show that the negative impact of cost variability is the product of two tendencies. First, experiencing periods with free updates slowed learning to select the optimal policy. Second, in many cases, the participants behaved as if they plan to update when the cost of updating is low but forget to do so. The results suggest that security can be enhanced by asking users to select a default updating policy before gaining experience and by replacing “free updates” with automatic updates. Information concerning the existence of automatic updates reduced manual updating, but this effect was eliminated by experience.

Bilevel programming approaches to production planning for multiple products with short life cycles

We provide decision-making models for a manufacturer which plans to produce multiple short life cycle products with the one-shot decision theory. The obtained optimal production quantities are based on the most appropriate scenarios for the manufacturer. Since the models are the bilevel programming problems with the max–min or min–max operator in the lower levels, we propose two approaches to translate them into general single-level optimization problems such that they can be solved via the commonly used optimization methods. The effectiveness of our approaches is examined from the theoretical and computational aspects.

Online task allocation in mobile cloud computing with budget constraints

Cloud computing offers task offloading opportunities for resource-constrained mobile devices. Such offloading comes with a round-trip communication delay that may be impractical for mobile applications. For controlling such delay, mobile cloud computing offloads user tasks to cloudlets at the edge of the Internet, which are close by. This work studies the allocation and scheduling of user applications for offloading to cloudlets. We first consider an offline scenario where a one-shot decision is made, towards maximizing revenue of the cloud provider. We employ a tailored rounding technique to design an efficient and effective approximation algorithm, for offline offloading. Then we further tackle the more practical and challenging online scenario, where users arrive stochastically, each subject to an overall offloading budget. We employ the one-round approximation algorithm as a building block in a primal-dual online optimization framework, and design an efficient online offloading algorithm that respects user budgets, while providing provable competitiveness in provider revenue.

An intuitionistic approach for one-shot decision making

An intuitionistic approach for one-shot decision making

The Impact of Dynamic Feedback and Personal Budgets on Arousal and Funding Behaviour in Participatory Budgeting

Public institutions as well as corporations seek to engage their constituents and employees in participatory processes to enhance engagement in decision-making. This paper proposes a group decision method of fusing crowdfunding and participatory budget allocation. In this approach, a central institution lets their members decide over budget allocation by endowing members with individual budgets. Participants are free to allocate their budgets to projects. A project is realized if its respective cost threshold is surpassed. We evaluate different design parameters of such mechanisms for group decisions and, based on this, the allocation of institutional budgets within a controlled laboratory experiment. The first design parameter is feedback on funding status, which can either be static (a one-shot decision, simultaneous funding) or dynamic (sequential decisions, repeated funding with continuous feedback). The second variable refers to the fraction of budget that may be kept privately by individuals and is not forfeit if not assigned to projects. Building on threshold public goods literature, we investigate how these parameters affect participants’ investment behaviour, their excitement, and overall welfare. We find that mechanisms including feedback net higher welfare gains as well as higher levels of arousal. Higher personal budget shares drive excitement but lead to lower welfare gains.

Behavioral models for first-price sealed-bid auctions with the one-shot decision theory

We build an auction model with the one-shot decision theory which describes the process of a bidder deciding his/her bidding price in first-price sealed-bid auctions. The decision making procedure involves two steps: First, for each of his/her possible bidding prices, the bidder examines every possible highest bidding price provided by the other bidders and chooses one as a focus point of this bidding price of him/her. Then, the bidder determines such a bidding price as the optimal one that generates the best outcome when its focus point occurs. The optimal bidding price can be obtained and two common phenomena in auction markets: throwing away and overbidding are well explained.

When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?

While optimal metareasoning is notoriously intractable, humans are nonetheless able to adaptively allocate their computational resources. A possible approximation that humans may use to do this is to only metareason over a finite set of cognitive systems that perform variable amounts of computation. The highly influential "dual-process" accounts of human cognition, which postulate the coexistence of a slow accurate system with a fast error-prone system, can be seen as a special case of this approximation. This raises two questions: how many cognitive systems should a bounded optimal agent be equipped with and what characteristics should those systems have? We investigate these questions in two settings: a one-shot decision between two alternatives, and planning under uncertainty in a Markov decision process. We find that the optimal number of systems depends on the variability of the environment and the costliness of metareasoning. Consistent with dual-process theories, we also find that when having two systems is optimal, then the first system is fast but error-prone and the second system is slow but accurate.

Spontaneous Metacognition in Rhesus Monkeys

Metacognition is the ability to think about thinking. Although monitoring and controlling one’s knowledge is a key feature of human cognition, its evolutionary origins are debated. In the current study, we examined whether rhesus monkeys (Macaca mulatta; N = 120) could make metacognitive inferences in a one-shot decision. Each monkey experienced one of four conditions, observing a human appearing to hide a food reward in an apparatus consisting of either one or two tubes. The monkeys tended to search the correct location when they observed this baiting event, but engaged in information seeking—by peering into a center location where they could check both potential hiding spots—if their view had been occluded and information seeking was possible. The monkeys only occasionally approached the center when information seeking was not possible. These results show that monkeys spontaneously use information about their own knowledge states to solve naturalistic foraging problems, and thus provide the first evidence that nonhumans exhibit information-seeking responses in situations with which they have no prior experience.

Newsvendor Models for Innovative Products with Inventory Inaccuracies

In recent years, there has been growing interest in the inventory inaccuracies in the newsvendor problems, which are costly and prevalent in many industries. Innovative products, which are characterized with short lifecycle and unpredictable demand, are especially suffered by the inventory inaccuracies. Based on the one-shot decision theory (OSDT), a scenario-based inventory model (newsvendor-OSDT) has been proposed for the newsvendor problems for innovative products. In this article, within the framework of newsvendor-OSDT, we consider two cases: (a) the retailer ignores the inventory errors; (b) RFID technology is used to reduce or eliminate the errors. Based on the theoretical analysis and computational simulation, the article aims at addressing the questions: does the inventory inaccuracy tackled using newsvendor-OSDT? Within the framework of newsvendor-OSDT, does the RFID contribute to the inventory inaccuracies? This article will construct the newsvendor models for innovative products with inventory inaccuracies and put forward managerial implications.

Possibilistic individual multi-period consumption–investment models

In this paper, we consider a multi-period consumption–investment problem with partially known information for an individual. We emphasize the fact that for an individual there is one and only one result will appear for his/her consumption–investment decision in each period. Different from the existing multi-period consumption–investment models which basically maximize the expected utility, we utilize the one-shot decision theory for individual multi-period consumption–investment problems. In each period, the individual chooses one state (scenario) for each strategy with considering the satisfaction of the outcome and its possibility. The selected state is called the focus point. The multi-period consumption–investment decision models with twelve types of focus points are built to maximize the sum of discounted consumption over the whole process (lifetime) for twelve types of individuals and economic insights are gained by the theoretical analysis. The proposed models are scenario-based decision models which provide a fundamental alternative to analyze individual multi-period consumption–investment behavior.

Newsvendor models for innovative products with one-shot decision theory

In this paper, newsvendor problems for innovative products are analyzed. Because the product is new, no relevant historical data is available for statistical demand analysis. Instead of using the probability distribution, the possibility distribution is utilized to characterize the uncertainty of the demand. We consider products whose life cycles are expected to be smaller than the procurement lead times. Determining optimal order quantities of such products is a typical one-shot decision problem for a retailer. Therefore, newsvendor models for innovative products are proposed based on the one-shot decision theory (OSDT). The main contributions of this research are as follows: the general solutions of active, passive, apprehensive and daring focus points and optimal alternatives are proposed and the existence theorem is established in the one-shot decision theory; a simple and effective approach for identifying the possibility distribution is developed; newsvendor models with four types of focus points are built; managerial insights into the behaviors of different types of retailers are gained by the theoretical analysis; the proposed models are scenario-based decision models which provide a fundamental alternative to analyze newsvendor problems for innovative products.