Towards the Design of Heuristics by Means of Self-Assembly

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly

HEURISTICS USED BY HUMANS WITH PREFRONTAL CORTEX DAMAGE: TOWARD AN EMPIRICAL MODEL OF PHINEAS GAGE

In many research contexts it is necessary to group experimental subjects into behavioral “types.” Usually, this is done by pre-specifying a set of candidate decision-making heuristics and then assigning each subject to the heuristic that best describes his/her behavior. Such approaches might not perform well when used to explain the behavior of subjects with prefrontal cortex damage. The reason is that introspection is typically used to generate the candidate heuristic set, but this procedure is likely to fail when applied to the decision-making strategies of subjects with brain damage. This research uses the type classification approach introduced by Houser, Keane and McCabe (2002) to investigate the heuristics used by subjects in the gambling experiment (Bechara, Damasio, Damasio and Anderson, 1994). An advantage of our classification approach is that it does not require us to specify the nature of subjects’ heuristics in advance. Rather, both the number and nature of the heuristics used are discerned directly from the experimental data. Our sample includes normal subjects, as well as subjects with damage to the ventromedial (VM) area of the prefrontal cortex. Subjects are “clustered” according to similarities in their heuristic, and this clustering does not preclude some normal and VM subjects from using the same decision rule. Our results are consistent with what others have found in subsequent experimentation with VM patients.experiments, heuristics, neuroeconomics, behavioral economics

A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.This work is supported by the National Nature Science Foundation (No. 69684005) and National High -Tech Program of P. R. China (No. 863-511-9609-003)

Behavioral Aspects of Pricing

Buyers sometimes exhibit seemingly “irrational” behavior with respect to prices and use socially embedded heuristics to simplify their purchase decisions. In some cases small changes in prices can lead to much larger than anticipated changes in sales and profitability. Sellers need to understand the heuristics consumers use, the situations in which they emerge, and recognize how they can respond in markets where information and knowledge of product attributes and competitive prices is increasingly available via the Internet. This chapter explores consumers’ behavioral reactions to price through a review of contemporary literature in the field of pricing. The chapter delineates the nature and scope of these effects based upon a critical review of the most up-to-date empirical research in the field, and concludes by providing implications for innovation in pricing, and guidance for managers to reduce the disconnect between themselves and consumers

On the Troll-Trust Model for Edge Sign Prediction in Social Networks

In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the troll-trust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple probabilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.Comment: v5: accepted to AISTATS 201

Fast, Frugal, and (Sometimes) Wrong

Do moral heuristics operate in the moral domain? If so, do they lead to moral errors? This brief essay offers an affirmative answer to both questions. In so doing, it responds to an essay by Gerd Gigerenzer on the nature of heuristics, moral and otherwise. While focused on morality, the discussion bears on the general debate between those who emphasize cognitive errors, sometimes produced by heuristics, and those who emphasize the frequent success of heuristics in producing sensible judgments in the real world. General claims are that it is contentious to see moral problems as ones of arithmetic, and that arguments about moral heuristics will often do well to steer clear of contentious arguments about what morality requires