Many moral judgments are rooted in the outrage heuristic. In making such judgments about certain personal injury cases, people's judgments are both predictable and widely shared. With respect to outrage (on a bounded scale of one to six) and punitive intent (also on a bounded scale of one to six), the judgments of one group of six people, or 12 people, nicely predict the judgements of other groups of six people, or 12 people. Moreover, outrage judgments are highly predictive of punitive intentions. Because of their use of the outrage heuristic, people are intuitive retributivists. People care about deterrence, but they do not think in terms of optimal deterrence. Because outrage is category-specific, those who use the outrage heuristic are likely to produce patterns that they would themselves reject, if only they were to see them. Because people are intuitive retributivists, they reject some of the most common and central understandings in economic and utilitarian theory. To the extent that a system of criminal justice depends on the moral psychology of ordinary people, it is likely to operate on the basis of the outrage heuristic and will, from the utilitarian point of view, end up making serious and systematic errors.

The diversity, ingenuity and differences of opinion displayed in the articles of the recent special issues on the recognition heuristic are testament to the power and theoretical fertility of a simple idea about the role of recognition in decision making. In this brief comment I mention a number of these papers, but my focus is on points of agreement and disagreement with the conclusions drawn by Gigerenzer and Goldstein (2011) in their review of a decade’s worth of research on the recognition heuristic.

Schelling (1969, 1971a,b, 1978) observed that macro-level patterns do not necessarily reflect micro-level intentions, desires or goals. In his classic model on neighborhood segregation which initiated a large and influential literature, individuals with no desire to be segregated from those who belong to other social groups nevertheless wind up clustering with their own type. Most extensions of Schelling’s model have replicated this result. There is an important mismatch, however, between theory and observation, which has received relatively little attention. Whereas Schelling-inspired models typically predict large degrees of segregation starting from virtually any initial condition, the empirical literature documents considerable heterogeneity in measured levels of segregation. This paper introduces a mechanism that can produce significantly higher levels of integration and, therefore, brings predicted distributions of segregation more in line with real-world observation. As in the classic Schelling model, agents in a simulated world want to stay or move to a new location depending on the proportion of neighbors they judge to be acceptable. In contrast to the classic model, agents’ classifications of their neighbors as acceptable or not depend lexicographically on recognition first and group type (e.g., ethnic stereotyping) second. The FACE-recognition model nests classic Schelling: When agents have no recognition memory, judgments about the acceptability of a prospective neighbor rely solely on his or her group type (as in the Schelling model). A very small amount of recognition memory, however, eventually leads to different classifications that, in turn, produce dramatic macro-level effects resulting in significantly higher levels of integration. A novel implication of the FACE-recognition model concerns the large potential impact of policy interventions that generate modest numbers of face-to-face encounters with members of other social groups.

What is the role of recognition in consumer choice? The recognition heuristic (RH) proposes that in situations where recognition is correlated with a decision criterion, recognized objects will be chosen more often than unrecognized ones, regardless of any other relevant information available about the recognized object. Past research has investigated this non-compensatory decision heuristic in inference. Here we report two experiments on preference using a naturalistic consumer choice task. Results revealed that, although recognition was a powerful driver of preferences, it was used in a compensatory rather than a non-compensatory way. Specifically, additional information learned about recognized brand objects significantly affected choices. It appears that recognition is processed in a compensatory manner and combined with other attributes in preferential choice.

Previous research on the role of recognition in decision-making in inferentialchoice has focussed on the Recognition Heuristic (RH), which proposes that insituations where recognition is predictive of a decision criterion, recognizedobjects will be chosen over unrecognized ones, regardless of any other availablerelevant information. In the current study we examine the role of recognition inpreferential choice, in which subjects had to choose one of a pair of consumerobjects that were presented with quality ratings (positive, neutral, andnegative). The results showed that subjects’ choices were largely basedon recognition, as the famous brand was preferred even when additional starratings rendered it as less attractive. However, the additional information didaffect the proportion of chosen famous items, in particular in the cases whenstar ratings for the recognised brand were negative. This condition alsoresulted in longer response times compared to neutral and positive conditions.Thus, the current data do not point to a simple compensatory mechanism inpreferential choice: although choice is affected by additional information, itseems that recognition is employed as an initial important first step in thedecision-making process.

Sexually reproducing pathogens such as Cyclospora cayetanensis often produce genetically heterogeneous infections where the number of unique sequence types detected at any given locus varies depending on which locus is sequenced. The genotypes assigned to these infections quickly become complex when additional loci are analysed. This genetic heterogeneity confounds the utility of traditional sequence-typing and phylogenetic approaches for aiding epidemiological trace-back, and requires new methods to address this complexity. Here, we describe an ensemble of two similarity-based classification algorithms, including a Bayesian and heuristic component that infer the relatedness of C. cayetanensis infections. The ensemble requires a set of haplotypes as input and assigns arbitrary distances to specimen pairs reflecting their most likely relationships. The approach was applied to data generated from a test cohort of 88 human fecal specimens containing C. cayetanensis, including 30 from patients whose infections were associated with epidemiologically defined outbreak clusters of cyclosporiasis. The ensemble assigned specimens to plausible clusters of genetically related infections despite their complex haplotype composition. These relationships were corroborated by a significant number of epidemiological linkages (P < 0.0001) suggesting the ensemble's utility for aiding epidemiological trace-back investigations of cyclosporiasis.

In this paper, we analyse the difference between two types of behavioural policies – nudges and boosts. We distinguish them on the basis of the mechanisms through which they are expected to operate and identify the contextual conditions that are necessary for each policy to be successful. Our framework helps judging which type of policy is more likely to bring about the intended behavioural outcome in a given situation.

Past research shows that candidates' racial identities influence the assumptions that voters draw about how they will behave in office. In a national survey experiment examining televised candidate advertisements, we find evidence that stereotypes differ both in their potency and how vulnerable they are to disconfirmation. Consistent with previous work, black candidates are broadly assumed to be more liberal than white candidates, although the effect is notably small in magnitude. Yet when it comes to more specific stereotypes—how black candidates will behave on individual issues—effects are not only much larger, but also more contingent on what information is available. We find that by providing a small bit of ideological information, black candidates can overcome the assumption that they will enact liberal policies as concerns taxation and non-racialized aspects of social welfare policy. But it is much more difficult for them to overturn the assumption that they will prioritize aid to minorities while in office.

With most tele-operated robots the operator's only feedback is the view from an onboard camera. Live video lets the operator observe the robot's immediate surroundings but does not establish the orientation or whereabouts of the robot in its environment. An additional plot of the robot's trajectory would be helpful for the operator and is sometimes provided, based on GPS. However, indoors where GPS is unavailable, tracking has to rely on dead-reckoning, which is too inaccurate to be useful. Our proposed TelOpTrak method corrects dead-reckoning errors even when only odometry and a low-cost (and thus, high-drift) MEMS-class gyro are available on the robot. TelOpTrak corrects gyro drift by exploiting the structured nature of most buildings, but without having to directly sense building features. This paper explains the TelOpTrak method and provides comprehensive experimental results.

Earlier versions of this paper (Borenstein et al., 2010a), (Borenstein et al., 2010b) were presented at two conferences. The main difference between the earlier conference papers and the present manuscript is that the latter is more comprehensive, more up-to-date, and it presents an entirely new set of experimental results, including results of a live demonstration at the 2010 Robotics Rodeo event at Ft. Benning, USA.

Embodiment design (ED) is an early phase of product development. ED problems consist of finding solution principles that satisfy product requirements such as physics behaviors and interactions between components. Constraint satisfaction techniques are useful to solve constraint-based models that are often partial, heterogeneous, and uncertain in ED. This paper proposes new constraint satisfaction techniques to tackle piecewise-defined physics phenomena or skill-based rules and multiple categories of variables arising in design applications. New search heuristics and a global piecewise constraint are introduced in the branch and prune framework. The capabilities of these techniques are illustrated with both academic and real-world problems. Complete models of the latter are presented.

We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is NP-hard. We propose mixed integer programming heuristics based on a planning horizon decomposition strategy to find a feasible solution. The planning horizon is partitioned into several sub-horizons over which a freezing or a relaxation strategy is applied. Some experimental results showing the effectiveness of the approach on real-world instances are presented. A sensitivity analysis on the parameters of the heuristics is reported.

We analyze the unit-demand Euclidean vehicle routeing problem, where n customers are modeled as independent, identically distributed uniform points and have unit demand. We show new lower bounds on the optimal cost for the metric vehicle routeing problem and analyze them in this setting. We prove that there exists a constant ĉ > 0 such that the iterated tour partitioning heuristic given by Haimovich and Rinnooy Kan (1985) is a (2 - ĉ)-approximation algorithm with probability arbitrarily close to 1 as the number of customers goes to ∞. It has been a longstanding open problem as to whether one can improve upon the factor of 2 given by Haimovich and Rinnooy Kan. We also generalize this, and previous results, to the multidepot case.

This study presents an application to optimize the use of an L-cut guillotine machine. The application has two distinct parts to it; first, a number of rectangular shapes are placed on as few metal sheets as possible by using genetic algorithms. Second, the sequence for cutting these pieces has to be generated. The guillotine's numeric control then uses this sequence to make the cuts.

The notion of treewidth is of considerable interest in relation to NP-hard problems.Indeed, several studies have shown that the tree-decomposition method can be used to solve many basic optimization problems in polynomialtime when treewidth is bounded, even if, for arbitrary graphs, computingthe treewidth is NP-hard.Several papers present heuristics with computational experiments.For many graphs the discrepancy between the heuristic resultsand the best lower bounds is still very large. The aim of this paper is to propose two new methodsfor computing the treewidth of graphs: a heuristic and a metaheuristic.The heuristic returns good results in a short computation time,whereas the metaheuristic (a Tabu search method)returns the best results known to have been obtained so far for all the DIMACS vertex coloring / treewidth benchmarks (a well-known collection of graphs used for both vertex coloring and treewidth problems.) Our results actually improve on the previous best results for treewidth problems in 53% of the cases. Moreover, we identify properties of the triangulation processto optimize the computing time of our method.