Analogy in Decision-Making

In the context of decision making under uncertainty, we formalize the concept of analogy: an analogy between two decision problems is a mapping that transforms one problem into the other while preserving the problem's structure. We identify the basic structure of a decision problem, and provide a representation of the mappings that pre- serve this structure. We then consider decision makers who use multiple analogies. Our main results are a representation theorem for "aggregators" of analogies satisfying certain minimal requirements, and the identification of preferences emerging from analogical reasoning. We show that a large variety of multiple-prior preferences can be thought of as emerging from analogical reasoning

Structured analogies for forecasting

When people forecast, they often use analogies but in an unstructured manner. We propose a structured judgmental procedure that involves asking experts to list as many analogies as they can, rate how similar the analogies are to the target situation, and match the outcomes of the analogies with possible outcomes of the target. An administrator would then derive a forecast from the experts' information. We compared structured analogies with unaided judgments for predicting the decisions made in eight conflict situations. These were difficult forecasting problems; the 32% accuracy of the unaided experts was only slightly better than chance. In contrast, 46% of structured analogies forecasts were accurate. Among experts who were independently able to think of two or more analogies and who had direct experience with their closest analogy, 60% of forecasts were accurate. Collaboration did not improve accuracy.accuracy, analogies, collaboration, conflict, expert, forecasting, judgment.

Structured Analogies for Forecasting

When people forecast, they often use analogies but in an unstructured manner. We propose a structured judgmental procedure that involves asking experts to list as many analogies as they can, rate how similar the analogies are to the target situation, and match the outcomes of the analogies with possible outcomes of the target. An administrator would then derive a forecast from the experts information. We compared structured analogies with unaided judgments for predicting the decisions made in eight conflict situations. These were difficult forecasting problems; the 32% accuracy of the unaided experts was only slightly better than chance. In contrast, 46% of structured analogies forecasts were accurate. Among experts who were independently able to think of two or more analogies and who had direct experience with their closest analogy, 60% of forecasts were accurate. Collaboration did not improve accuracy.accuracy, analogies, collaboration, conflict, expert, forecasting, judgment.

Rationality in context: An analogical perspective

At times, human behavior seems erratic and irrational. Therefore, when modeling human decision-making, it seems reasonable to take the remarkable abilities of humans into account with respect to rational behavior, but also their apparent deviations from the normative standards of rationality shining up in certain rationality tasks. Based on well-known challenges for human rationality, together with results from psychological studies on decision-making and from previous work in the field of computational modeling of analogy-making, I argue that the analysis and modeling of rational belief and behavior should also consider context-related cognitive mechanisms like analogy-making and coherence maximization of the background theory. Subsequently, I conceptually outline a high-level algorithmic approach for a Heuristic Driven Theory Projection-based system for simulating context-dependent human-style rational behavior. Finally, I show and elaborate on the close connections, but also on the significant differences, of this approach to notions of "ecological rationality"

PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤

Structured analogies for forecasting

People often use analogies when forecasting, but in an unstructured manner. We propose a structured judgmental procedure whereby experts list analogies, rate their similarity to the target, and match outcomes with possible target outcomes. An administrator would then derive a forecast from the information. When predicting decisions made in eight conflict situations, unaided experts\u27 forecasts were little better than chance, at 32% accurate. In contrast, 46% of structured-analogies forecasts were accurate. Among experts who were able to think of two or more analogies and who had direct experience with their closest analogy, 60% of forecasts were accurate. Collaboration did not help

Structured analogies in forecasting

When people forecast, they often use analogies but in an unstructured manner. We propose a structured judgmental procedure that involves asking experts to list as many analogies as they can, rate how similar the analogies are to the target situation, and match the outcomes of the analogies with possible outcomes of the target. An administrator would then derive a forecast from the experts’ information. We compared structured analogies with unaided judgments for predicting the decisions made in eight conflict situations. These were difficult forecasting problems; the 32% accuracy of the unaided experts was only slightly better than chance. In contrast, 46% of structured analogies forecasts were accurate. Among experts who were independently able to think of two or more analogies and who had direct experience with their closest analogy, 60% of forecasts were accurate. Collaboration did not improve accuracy