An Ambiguity Aversion Framework of Security Game under Ambiguities

Security is a critical concern around the world. Since resources for security are always limited, lots of interest have arisen in using game theory to handle security resource allocation problems. However, most of the existing work does not address adequately how a defender chooses his optimal strategy in a game with absent, inaccurate, uncertain, and even ambiguous strategy profiles' payoffs. To address this issue, we propose a general framework of security games under ambiguities based on Dempster-Shafer theory and the ambiguity aversion principle of minimax regret. Then, we reveal some properties of this framework. Also, we present two methods to reduce the influence of complete ignorance. Our investigation shows that this new framework is better in handling security resource allocation problems under ambiguities.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

Regret as autobiographical memory

We apply all autobiographical memory framework to the Study of regret. Focusing oil the distinction between regrets for specific and general events we argue that the temporal profile of regret, usually explained in terms of the action-inaction distinction, is predicted by models of autobiographical memory. In two studies involving Participants in their sixties we demonstrate a reminiscence bump for general, but not for specific regrets. Recent regrets were more likely to be specific than general in nature. Coding regrets as actions/inactions revealed that general regrets were significantly more likely to be due to inaction while specific regrets were as likely to be clue to action as to inaction. In Study 2 we also generalised all of these findings to a group of participants in their 40s. We re-interpret existing accounts of the temporal profile of regret within the autobiographical memory framework, and Outline the practical and theoretical advantages Of Our memory-based distinction over traditional decision-making approaches to the Study of regret. (C) 2008 Elsevier Inc. All rights reserved.

Comparisons, mental models, and the action effect in judgments of regret

People tend to attribute more regret to a character who has decided to take action and experienced a negative outcome than to one who has decided not to act and experienced a negative outcome. For some decisions, however, this finding is not observed in a between-participants design and thus appears to rely on comparisons between people's representations of action and their representations of inaction. In this article, we outline a mental models account that explains findings from studies that have used within- and between-participants designs, and we suggest that, for decisions with uncertain counterfactual outcomes, information about the consequences of a decision to act causes people to flesh out their representation of the counterfactual states of affairs for inaction. In three experiments, we confirm our predictions about participants' fleshing out of representations, demonstrating that an action effect occurs only when information about the consequences of action is available to participants as they rate the nonactor and when this information about action is informative with respect to judgments about inaction. It is important to note that the action effect always occurs when the decision scenario specifies certain counterfactual outcomes. These results suggest that people sometimes base their attributions of regret on comparisons among different sets of mental models.

Is regret for inaction relatively self-enhancing?

Previous accounts of regret suggest that people report greater regret for inaction than for action because the former is longer lasting and more painful than the latter. We suggest instead that the tendency for people's greatest regrets to concern inaction more than action may be due to the relatively self-enhancing nature of regrets for inaction. In Study I we asked people to think about their greatest recent regret and to code it as being due to action or inaction. In Study 2 participants described their greatest regret from across their entire life. In both studies we observed an inaction effect only amongst individuals high in self-esteem (HSE). In Study 2 we found that the inaction effect was confined to HSE people whose greatest regret was personal in nature. These results support the claim that regret for inaction is relatively self-enhancing and suggest that the inaction effect found in real-life regrets may be due, in part at least, to the self-enhancement goals of HSE individuals. Copyright (c) 2005 John Wiley & Sons, Ltd.

The development of regret

In two experiments, 4- to 9-year-olds played a game in which they
selected one of two boxes to win a prize. On regret trials the unchosen
box contained a better prize than the prize children actually
won, and on baseline trials the other box contained a prize of the
same value. Children rated their feelings about their prize before
and after seeing what they could have won if they had chosen
the other box and were asked to provide an explanation if their
feelings had changed. Patterns of responding suggested that regret
was experienced by 6 or 7 years of age; children of this age could
also explain why they felt worse in regret trials by referring to
the counterfactual situation in which the prize was better. No evidence
of regret was found in 4- and 5-year-olds. Additional findings
suggested that by 6 or 7 years, children’s emotions were
determined by a consideration of two different counterfactual
scenarios.

Site choices in recreational demand: a matter of utility maximization or regret minimization?

This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.

Random Regret Minimization: Exploration of a New Choice Model for Environmental and Resource Economics

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random Utility Maximization-theory (RUM-theory). We highlight how RRM-based models provide closed form, logit-type formulations for choice probabilities that allow for capturing semi-compensatory behaviour and choice set-composition effects while being equally parsimonious as their utilitarian counterparts. Using data from a Stated Choice-experiment aimed at identifying valuations of characteristics of nature parks, we compare RRM-based models and RUM-based models in terms of parameter estimates, goodness of fit, elasticities and consequential policy implications.

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

This paper introduces the discrete choice model-paradigm of Random Regret Minimisation (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximisation (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimise their regret–regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.

Stated choices and benefit estimates in the context of traffic calming schemes:Utility maximization, regret minimization, or both?

This paper proposes a discrete mixture model which assigns individuals, up to a probability, to either a class of random utility (RU) maximizers or a class of random regret (RR) minimizers, on the basis of their sequence of observed choices. Our proposed model advances the state of the art of RU-RR mixture models by (i) adding and simultaneously estimating a membership model which predicts the probability of belonging to a RU or RR class; (ii) adding a layer of random taste heterogeneity within each behavioural class; and (iii) deriving a welfare measure associated with the RU-RR mixture model and consistent with referendum-voting, which is the adequate mechanism of provision for such local public goods. The context of our empirical application is a stated choice experiment concerning traffic calming schemes. We find that the random parameter RU-RR mixture model not only outperforms its fixed coefficient counterpart in terms of fit-as expected-but also in terms of plausibility of membership determinants of behavioural class. In line with psychological theories of regret, we find that, compared to respondents who are familiar with the choice context (i.e. the traffic calming scheme), unfamiliar respondents are more likely to be regret minimizers than utility maximizers. © 2014 Elsevier Ltd.

Do Children Who Experience Regret Make Better Decisions? A Developmental Study of the Behavioral Consequences of Regret

Although regret is assumed to facilitate good decision making, there is little research directly addressing this assumption. Four experiments (N = 326) examined the relation between children's ability to experience regret and the quality of their subsequent decision making. In Experiment 1 regret and adaptive decision making showed the same developmental profile, with both first appearing at about 7 years. In Experiments 2a and 2b, children aged 6–7 who experienced regret decided adaptively more often than children who did not experience regret, and this held even when controlling for age and verbal ability. Experiment 3 ruled out a memory-based interpretation of these findings. These findings suggest that the experience of regret facilitates children's ability to learn rapidly from bad outcomes.

The development of the experience and anticipation of regret

Although recent studies have established that children experience regret from around 6 years, we do not yet know when the ability to anticipate this emotion emerges, despite the importance of the anticipation of regret in decision-making. We examined whether children will anticipate they will feel regret if they were to find out in a box-choosing game that, had they made a different choice, they would have obtained a better prize. Experiment 1 replicated Guttentag and Ferrell’s study in which children were asked what they hoped was in a non-chosen box. Even 8- to 9-year olds find this question difficult. However, when asked what might make them feel sadder, 7- to 8-year olds (but not younger children) predicted that finding the larger prize in the unchosen box would make them feel this way. In Experiments 2 and 3, children predicted how they would feel if the unchosen box contained either a larger or smaller prize, in order to examine anticipation of both regret and of relief. Although 6- to 7-year olds do experience regret when they find out they could have won a better prize, they do not correctly anticipate feeling this way. By around 8 years, the majority of children are able to anticipate both regret and relief.

Regret minimisation and utility maximisation in a freight transport context

This study is the first to compare random regret minimisation (RRM) and random utility maximisation (RUM) in freight transport application. This paper aims to compare RRM and RUM in a freight transport scenario involving negative shock in the reference alternative. Based on data from two stated choice experiments conducted among Swiss logistics managers, this study contributes to related literature by exploring for the first time the use of mixed logit models in the most recent version of the RRM approach. We further investigate two paradigm choices by computing elasticities and forecasting choice probability. We find that regret is important in describing the managers’ choices. Regret increases in the shock scenario, supporting the idea that a shift in reference point can cause a shift towards regret minimisation. Differences in elasticities and forecast probability are identified and discussed appropriately.

Regret and adaptive decision making in young children

In line with the claim that regret plays a role in decision making, O’Connor, McCormack, and Feeney (2014) found that children who reported feeling sadder on discovering they had made a non-optimal choice were more likely to make a different choice next time round. We examined two issues of interpretation regarding this finding: whether the emotion measured was indeed regret, and whether it was the experience of this emotion rather than the ability to anticipate it that impacted on decision making. To address the first issue, we varied the degree to which children aged 6-7 were responsible for an outcome, assuming that responsibility is a necessary condition for regret. The second was addressed by examining whether children could accurately anticipate that they would feel worse on discovering they had made a non-optimal choice. Children were more likely to feel sad if they were responsible for the outcome; however even if they were not responsible, children were more likely than chance to report feeling sadder. Moreover, across all conditions feeling sadder was associated with making a better subsequent choice. In a separate task, we demonstrated that children of this age cannot accurately anticipate feeling sadder on discovering that they had not made the best choice. These findings suggest that although children may feel regret following a non-optimal choice, even if they were not responsible for an outcome they may experience another negative emotion such as frustration. Experiencing either of these emotions seems to be sufficient to support better decision making.