On heuristic and linear models of judgment: Mapping the demand for knowledge

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from as if linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of lens model research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human andheuristic performance in the same tasks. Our results highlight the trade-off betweenlinear models and heuristics. Whereas the former are cognitively demanding, the latterare simple to use. However, they require knowledge and thus maps of when andwhich heuristic to employ.

Évolution des projets de formation de futurs enseignants du primaire au contact de situations probabilistes

Il semble y avoir des attentes réciproques non comblées en formation initiale à l’enseignement des mathématiques. Cherchant à comprendre la genèse de ces attentes, nous nous sommes intéressée à la vision que les étudiants nourrissent des phénomènes d’enseignement. Ayant postulé que les étudiants ont une vision déterministe de ces phénomènes, et considérant que leur anticipation oriente leur projet de formation, nous nous sommes attaquée au problème de la rencontre des projets des étudiants et des formateurs. Deux objectifs généraux ont été formulés : le premier concerne la description des projets de formation des étudiants tandis que le second concerne l’expérimentation d’une séquence de situations susceptible de faire évoluer leurs projets. Cette recherche a été menée auprès de 58 étudiants du baccalauréat en enseignement en adaptation scolaire et sociale d’une même université, lesquels entamaient leur formation initiale à l’enseignement des mathématiques. Afin d’explorer les projets qu’ils nourrissent a priori, tous les étudiants ont complété un questionnaire individuel sur leur vision des mathématiques et de leur enseignement et ont participé à une première discussion de groupe sur le sujet. Une séquence de situations probabilistes leur a ensuite été présentée afin d’induire une complexification de leur projet. Enfin, cette expérimentation a été suivie d’une seconde discussion de groupe et complétée par la réalisation de huit entretiens individuels. Il a été mis en évidence que la majorité des étudiants rencontrés souhaitent avant tout évoluer en tant qu’enseignant, en développant leur capacité à enseigner et à faire apprendre ou comprendre les mathématiques. Bien que certaines visées se situent dans une perspective transmissive, celles-ci ne semblent pas représentatives de l’ensemble des projets "visée". De plus, même si la plupart des étudiants rencontrés projettent de développer des connaissances relatives aux techniques et aux méthodes d’enseignement, la sensibilité à la complexité dont certains projets témoignent ne permet plus de réduire les attentes des étudiants à l’endroit de leur formation à la simple constitution d’un répertoire de techniques d’enseignement réputées efficaces. En ce qui a trait aux modes d’anticipation relevés a priori, nos résultats mettent en relief des anticipations se rattachant d’abord à un mode adaptatif, puis à un mode prévisionnel. Aucune anticipation se rattachant à un mode prospectif n’a été recensée a priori. La séquence a permis aux étudiants de s’engager dans une dialectique d’action, de formulation et de validation, elle les a incités à recourir à une approche stochastique ainsi qu’à porter un jugement de probabilité qui prenne en compte la complexité de la situation. A posteriori, nous avons observé que les projets "visée" de certains étudiants se sont complexifiés. Nous avons également noté un élargissement de la majorité des projets, lesquels considèrent désormais les autres sommets du triangle didactique. Enfin, des anticipations se rattachant à tous les modes d’anticipation ont été relevées. Des anticipations réalisées grâce à un mode prospectif permettent d’identifier des zones d’incertitude et de liberté sur lesquelles il est possible d’agir afin d’accroître la sensibilité à la complexité des situations professionnelles à l’intérieur desquelles les futurs enseignants devront se situer.

What We Don't Know Can Hurt Us: Clinical Inference Bias and Constructed Memories of Sexual Abuse.

The false memory/recovered memory debate, research regarding the malleability of memory, and the current lack of methods for validating recovered memories all support the view that heightened care is required of therapists dealing with clients whom they suspect have been sexually abused. The judgmental heuristics that underlie the major clinical inference biases of confirmatory bias, biased covariation, base rate fallacies, and schematic processing errors are all relevant to the processes leading to therapist-client constructions of memories of sexual abuse. Suggestions for minimizing each of these biases are offered. Personal motivations of the client and client suggestibility are factors that may contribute to the construction of memories of sexual abuse, and suggestions for minimizing the impact of these motivations are offered. In conclusion, general suggestions for minimizing the impact of clinical inference biases within the sexual abuse treatment context are summarized.

New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs. Published by Elsevier Ltd.

Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Examination timetabling automation using hybrid meta-heuristics

Trabalho de projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Heuristics and policies for online pickup and delivery problems

Master Thesis