How to improve the formal analytic reasoning and scientific motivation in the intellectual training of medical students

A review article of the The New England Journal of Medicine refers that almost a century ago, Abraham Flexner, a research scholar at the Carnegie Foundation for the Advancement of Teaching, undertook an assessment of medical education in 155 medical schools in operation in the United States and Canada. Flexner’s report emphasized the nonscientific approach of American medical schools to preparation for the profession, which contrasted with the university-based system of medical education in Germany. At the core of Flexner’s view was the notion that formal analytic reasoning, the kind of thinking integral to the natural sciences, should hold pride of place in the intellectual training of physicians. This idea was pioneered at Harvard University, the University of Michigan, and the University of Pennsylvania in the 1880s, but was most fully expressed in the educational program at Johns Hopkins University, which Flexner regarded as the ideal for medical education. (...)

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Does Reasoning Enhance Learning?

Utilizing the well-known Ultimatum Game, this note presents the following phenomenon. If we start with simple stimulus-response agents, learning through naive reinforcement, and then grant them some introspective capabilities, we get outcomes that are not closer but farther away from the fully introspective game-theoretic approach. The cause of this is the following: there is an asymmetry in the information that agents can deduce from their experience, and this leads to a bias in their learning process.

Rational reasoning or adaptive behavior? Evidence from two-person beauty contest games

Many experiments have shown that human subjects do not necessarily behave in line with game theoretic assumptions and solution concepts. The reasons for this non-conformity are multiple. In this paper we study the argument whether a deviation from game theory is because subjects are rational, but doubt that others are rational as well, compared to the argument that subjects, in general, are boundedly rational themselves. To distinguish these two hypotheses, we study behavior in repeated 2-person and many-person Beauty-Contest-Games which are strategically different from one another. We analyze four different treatments and observe that convergence toward equilibrium is driven by learning through the information about the other player s choice and adaptation rather than self-initiated rational reasoning.

Endogenous depth of reasoning

We introduce a model of strategic thinking in games of initial response. Unlike standard level-k models, in this framework the player's `depth of reasoning' is endogenously determined, andit can be disentangled from his beliefs over his opponent's cognitive bound. In our approach,individuals act as if they follow a cost-benefit analysis. The depth of reasoning is a function ofthe player's cognitive abilities and his payoffs. The costs are exogenous and represent the gametheoretical sophistication of the player; the benefit instead is related to the game payoffs. Behavioris in turn determined by the individual's depth of reasoning and his beliefs about the reasoningprocess of the opponent. Thus, in our framework, payoffs not only affect individual choices inthe traditional sense, but they also shape the cognitive process itself. Our model delivers testableimplications on players' chosen actions as incentives and opponents change. We then test themodel's predictions with an experiment. We administer different treatments that vary beliefs overpayoffs and opponents, as well as beliefs over opponents' beliefs. The results of this experiment,which are not accounted for by current models of reasoning in games, strongly support our theory.Our approach therefore serves as a novel, unifying framework of strategic thinking that allows forpredictions across games.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Adding similarity-based reasoning capabilities to a Horn fragment of possibilistic logic with fuzzy constants

PLFC is a first-order possibilistic logic dealing with fuzzy constants and fuzzily restricted quantifiers. The refutation proof method in PLFC is mainly based on a generalized resolution rule which allows an implicit graded unification among fuzzy constants. However, unification for precise object constants is classical. In order to use PLFC for similarity-based reasoning, in this paper we extend a Horn-rule sublogic of PLFC with similarity-based unification of object constants. The Horn-rule sublogic of PLFC we consider deals only with disjunctive fuzzy constants and it is equipped with a simple and efficient version of PLFC proof method. At the semantic level, it is extended by equipping each sort with a fuzzy similarity relation, and at the syntactic level, by fuzzily “enlarging” each non-fuzzy object constant in the antecedent of a Horn-rule by means of a fuzzy similarity relation.

Formalizing argumentative reasoning in a possibilistic logic programming setting with fuzzy unification

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at the object-language level. In spite of its expressive power, an important limitation in P-DeLP is that imprecise, fuzzy information cannot be expressed in the object language. One interesting alternative for solving this limitation is the use of PGL+, a possibilistic logic over Gödel logic extended with fuzzy constants. Fuzzy constants in PGL+ allow expressing disjunctive information about the unknown value of a variable, in the sense of a magnitude, modelled as a (unary) predicate. The aim of this article is twofold: firstly, we formalize DePGL+, a possibilistic defeasible logic programming language that extends P-DeLP through the use of PGL+ in order to incorporate fuzzy constants and a fuzzy unification mechanism for them. Secondly, we propose a way to handle conflicting arguments in the context of the extended framework.

A Case-based reasoning approach to validate grammatical gender and number agreement in Spanish language

Across Latin America 420 indigenous languages are spoken. Spanish is considered a second language in indigenous communities and is progressively introduced in education. However, most of the tools to support teaching processes of a second language have been developed for the most common languages such as English, French, German, Italian, etc. As a result, only a small amount of learning objects and authoring tools have been developed for indigenous people considering the specific needs of their population. This paper introduces Multilingual–Tiny as a web authoring tool to support the virtual experience of indigenous students and teachers when they are creating learning objects in indigenous languages or in Spanish language, in particular, when they have to deal with the grammatical structures of Spanish. Multilingual–Tiny has a module based on the Case-based Reasoning technique to provide recommendations in real time when teachers and students write texts in Spanish. An experiment was performed in order to compare some local similarity functions to retrieve cases from the case library taking into account the grammatical structures. As a result we found the similarity function with the best performance

Collusion in growing and shrinking markets: empirical evidence from experimental duopolies

We study collusive behaviour in experimental duopolies that compete in prices under dynamic demand conditions. In one treatment the demand grows at a constant rate. In the other treatment the demand declines at another constant rate. The rates are chosen so that the evolution of the demand in one case is just the reverse in time than the one for the other case. We use a box-design demand function so that there are no issues of finding and co-ordinating on the collusive price. Contrary to game-theoretic reasoning, our results show that collusion is significantly larger when the demand shrinks than when it grows. We conjecture that the prospect of rapidly declining profit opportunities exerts a disciplining effect on firms that facilitates collusion and discourages deviation.

A foundation model for marxian breakdown theories based on a new falling rate of profit mechanism (Long version)

The paper presents a foundation model for Marxian theories of the breakdown of capitalism based on a new falling rate of profit mechanism. All of these theories are based on one or more of "the historical tendencies": a rising capital-wage bill ratio, a rising capitalist share and a falling rate of profit. The model is a foundation in the sense that it generates these tendencies in the context of a model with a constant subsistence wage. The newly discovered generating mechanism is based on neo-classical reasoning for a model with land. It is non-Ricardian in that land augmenting technical progress can be unboundedly rapid. Finally, since the model has no steady state, it is necessary to use a new technique, Chaplygin's method, to prove the result.

A foundation model for marxian breakdown theories based on a new falling rate of profit mechanism.

The paper presents a foundation model for Marxian theories of the breakdown of capitalism based on a new falling rate of profit mechanism. All of these theories are based on one or more of ?the historical tendencies?: a rising capital-wage bill ratio, a rising capitalist share and a falling rate of profit. The model is a foundation in the sense that it generates these tendencies in the context of a model with a constant subsistence wage. The newly discovered generating mechanism is based on neo-classical reasoning for a model with land. It is non-Ricardian in that land augmenting technical progress can be unboundedly rapid. Finally, since the model has no steady state, it is necessary to use a new technique, Chaplygin?s method, to prove the result.

Model-based diagnosis system: application to constructed wetlands

Report for the scientific sojourn carried out at the Model-based Systems and Qualitative Reasoning Group (Technical University of Munich), from September until December 2005. Constructed wetlands (CWs), or modified natural wetlands, are used all over the world as wastewater treatment systems for small communities because they can provide high treatment efficiency with low energy consumption and low construction, operation and maintenance costs. Their treatment process is very complex because it includes physical, chemical and biological mechanisms like microorganism oxidation, microorganism reduction, filtration, sedimentation and chemical precipitation. Besides, these processes can be influenced by different factors. In order to guarantee the performance of CWs, an operation and maintenance program must be defined for each Wastewater Treatment Plant (WWTP). The main objective of this project is to provide a computer support to the definition of the most appropriate operation and maintenance protocols to guarantee the correct performance of CWs. To reach them, the definition of models which represent the knowledge about CW has been proposed: components involved in the sanitation process, relation among these units and processes to remove pollutants. Horizontal Subsurface Flow CWs are chosen as a case study and the filtration process is selected as first modelling-process application. However, the goal is to represent the process knowledge in such a way that it can be reused for other types of WWTP.

Reactive and proactive approaches for introspective CBR

This work investigates applying introspective reasoning to improve the performance of Case-Based Reasoning (CBR) systems, in both reactive and proactive fashion, by guiding learning to improve how a CBR system applies its cases and by identifying possible future system deficiencies. First we present our reactive approach, a new introspective reasoning model which enables CBR systems to autonomously learn to improve multiple facets of their reasoning processes in response to poor quality solutions. We illustrate our model’s benefits with experimental results from tests in an industrial design application. Then as for our proactive approach, we introduce a novel method for identifying regions in a case-base where the system gives low confidence solutions to possible future problems. Experimentation is provided for Zoology and Robo-Soccer domains and we argue how encountered regions of dubiosity help us to analyze the case-bases of a given CBR system.