Maximizing versus matching on concurrent variable-interval schedules

Maximization and matching predictions were examined for a time-based analogue of the concurrent variable-interval variable-ratio schedule. One alternative was a variable interval whose time base operated relatively independent of the schedule chosen, and the other was a discontinuous variable interval for which timing progressed only when selected. Pigeons switched between schedules by pecking a changeover key. The maximization hypothesis predicts that subjects will show a bias toward the discontinuous variable interval and undermatching; however the obtained results conformed closely to the predictions of the matching law. Finally, a quantitative comparison was made of the bias and sensitivity estimates obtained in published concurrent variable-interval variable-ratio analogue studies. Results indicated that only the ratio-based analogue of the concurrent variable interval variable ratio studied by Green, Rachlin, and Hanson (1983) produced significant bias toward the variable-ratio alternative and undermatching, as predicted by reinforcement maximization.     

An automated test development of parallel tests from a seed test

Binary programming models are presented to generate parallel tests from an itembank. The parallel tests are created to match item for item an existing seed test and match user supplied taxonomic specifications. The taxonomic specifications may be either obtained from the seed test or from some other user requirement. An algorithm is presented along with computational results to indicate the overall efficiency of the process. Empirical findings based on an itembank for the Arithmetic Reasoning section of the Armed Services Vocational Aptitude Battery are given.The Office of Naval Research, Program in Cognitive Science, N00014-87-C-0696 partially supported the work of Douglas H. Jones. The Rutgers Research Resource Committee of the Graduate School of Management partially supported the work of Douglas H. Jones and Ing-Long Wu. A Thomas and Betts research fellowship partially supported the work of Ing-Long Wu. The Human Resources Laboratory, United States Air Force, partially supported the work of Ronald Armstrong. The authors benefited from conversations with Dr. Wayne Shore, Operational Technologies, San Antonio, Texas. The order of authors' names is alphabetical and denotes equal authorship.     

Minimizing the number of observations: A generalization of the spearman-brown formula

A new method for determining the minimum number of observations per subject needed to achieve a specific generalizability coefficient is presented. This method, which consists of a branch-and-bound algorithm, allows for the employment of constraints specified by the investigator.     

More on concurrent interval-ratio schedules: a replication and review.

It has been suggested that the failure to maximize reinforcement on concurrent variable-interval, variable-ratio schedules may be misleading. Inasmuch as response costs are not directly measured, it is possible that subjects are optimally balancing the benefits of reinforcement against the costs of responding. To evaluate this hypothesis, pigeons were tested in a procedure in which interval and ratio schedules had equal response costs. On a concurrent variable time (VT), variable ratio-time (VRT) schedule, the VT schedule runs throughout the session and the VRT schedule is controlled by responses to a changeover key that switches from one schedule to the other. Reinforcement is presented independent of response. This schedule retains the essential features of concurrent VI VR, but eliminates differential response costs for the two alternatives. It therefore also eliminates at least one significant ambiguity about the reinforcement maximizing performance. Pigeons did not maximize rate of reinforcement on this procedure. Instead, their times spent on the alternative schedules matched the relative rates of reinforcement, even when schedule parameters were such that matching earned the lowest possible overall rate of reinforcement. It was further shown that the observed matching was not a procedural artifact arising from the constraints built into the schedule.     

Behavior regulation and learned performance: Some misapprehensions and disagreements

The behavior-regulation approach to learned performance has been the subject of misapprehension and disagreement concerning: (1) the nature and importance of behavior regulation, (2) the definition and role of behavioral set-points, (3) the relation of optimal schedule performance to behavioral set-points, and (4) the question of whether deviations from total responding or from response patterns are the primary determinant of molar responding under schedule constraint. After clarifying the nature and role of behavior regulation and set-points, this paper shows that the data used to question optimal schedule performance (Allison, 1981a) actually strongly support the general behavior-regulation approach. These data also indicate a role for response-pattern set-points in determining schedule behavior, but contradict the hypothesis that deviations from response-pattern characteristics are the primary determinant of molar schedule effects.     

Relative costs and statistical power in the extreme groups approach

The relationship between variables in applied and experimental research is often investigated by the use of extreme (i.e., upper and lower) groups. Recent analytical work has provided an extreme groups procedure that is more powerful than the standard correlational approach for all values of the correlation and extreme group size. The present article provides procedures to optimize power by determining the relative number of subjects to use in each of two stages of data collection given a fixed testing budget.The opinions or assertions contained herein are those of the writers and are not to be construed as official or reflecting the views of the Navy Department.Edward F. Alf, Jr. is also affiliated with the Department of Psychology, San Diego State University.     

How to maximize reward rate on two variable-interval paradigms

Without assuming any constraints on behavior, we derive the policy that maximizes overall reward rate on two variable-interval paradigms. The first paradigm is concurrent variable time-variable time with changeover delay. It is shown that for nearly all parameter values, a switch to the schedule with the longer interval should be followed immediately by a switch back to the schedule with the shorter interval. The matching law does not hold at the optimum and does not uniquely specify the obtained reward rate. The second paradigm is discrete trial concurrent variable interval-variable interval. For given schedule parameters, the optimal policy involves a cycle of a fixed number of choices of the schedule with the shorter interval followed by one choice of the schedule with the longer interval. Molecular maximization sometimes results in optimal behavior.     

Indclus: An individual differences generalization of the adclus model and the mapclus algorithm

We present a new model and associated algorithm, INDCLUS, that generalizes the Shepard-Arabie ADCLUS (ADditive CLUStering) model and the MAPCLUS algorithm, so as to represent in a clustering solution individual differences among subjects or other sources of data. Like MAPCLUS, the INDCLUS generalization utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach to impose discrete (0,1) constraints on parameters defining cluster membership. All subjects in an INDCLUS analysis are assumed to have a common set of clusters, which are differentially weighted by subjects in order to portray individual differences. As such, INDCLUS provides a (discrete) clustering counterpart to the Carroll-Chang INDSCAL model for (continuous) spatial representations. Finally, we consider possible generalizations of the INDCLUS model and algorithm.We are indebted to Seymour Rosenberg for making available the data from Rosenberg and Kim [1975]. Also, this work has benefited from the observations of S. A. Boorman, W. S. DeSarbo, G. Furnas, P. E. Green, L. J. Hubert, L. E. Jones, J. B. Kruskal, S. Pruzansky, D. Schmittlein, E. J. Shoben, S. D. Soli, and anonymous referees.This research was supported in part by NSF Grant SES82 00441, LEAA Grant 78-NI-AX-0142, and NSF Grant SES80 04815.     

Choice, changeover, and travel

Since foraging in nature can be viewed as instrumental behavior, choice between sources of food, known as “patches,” can be viewed as choice between instrumental response alternatives. Whereas the travel required to change alternatives deters changeover in nature, the changeover delay (COD) usually deters changeover in the laboratory. In this experiment, pigeons were exposed to laboratory choice situations, concurrent variable-interval schedules, that were standard except for the introduction of a travel requirement for changeover. As the travel requirement increased, rate of changeover decreased and preference for a favored alternative strengthened. When the travel requirement was small, the relations between choice and relative reinforcement revealed the usual tendencies toward matching and undermatching. When the travel requirement was large, strong overmatching occurred. These results, together with those from experiments in which changeover was deterred by punishment or a fixed-ratio requirement, deviate from the matching law, even when a correction is made for cost of changeover. If one accepted an argument that the COD is analogous to travel, the results suggest that the norm in choice relations would be overmatching. This overmatching, however, might only be the sign of an underlying strategy approximating optimization.     

Strategic Revenue Management under Uncertainty: A Case Study on Real Estate Projects in India

This paper proposes an approach for strategic revenue management under uncertainty for real estate projects. It integrates three modelling techniques: first, artificial neural network integrated support vector machines for forecasting the profit and loss‐making real estate residential projects; second, analytical network process approach using decision making trials and evaluation laboratory methodology for establishing interrelationships among factors; and third, multiobjective genetic algorithm approach for obtaining optimal numbers and types of apartments in a real estate project. We compare the respective revenues generated with the new number of apartments and price from the suggested revenue maximization model and that of the old practiced one through a case study of India. Copyright © 2015 John Wiley & Sons, Ltd.