Cognitive styles measured by the Myers-Briggs indicator were studied to isolate how style influences decision behavior. An experiment was conducted in which experienced decision makers from hospitals and firms were asked to assess several capital expansion projects. To control for environmental factors, the project summaries were tailored to be compatible or incompatible with each individual's cognitive style. Risk and information sources were also controlled in the summary by using two levels of risk, defined by the spread of the return on investment (ROI) projections, and by using personal judgements or a computer-based model to provide the ROI estimates. The decision makers assessed each project, indicating their likelihood of adopting it and their perception of its risk. The impact of style, setting (hospital or firm), and the control factors (environment, information source, and risk) were related to a decision to adopt and assessments of risk by ANOVA techniques. Cognitive style was found to be an important factor in the decision to adopt and the assessment of risk. ST (sensation-thinking) styles saw the highest risk and were reluctant to adopt the projects, while SF (sensation-feeling) styles were risk tolerant and more likely to adopt the same projects. Our results support the views of cognitive theorists, who argue that decision style is an important determinant of behavior. Decisions seem to be a function of the decision maker's cognitive makeup which differs for different psychological types.

The well known Sharpe-Lintner-Mossin capital asset pricing model (CAPM) assumes the existence of stability in the price level so that the market price of risk (MPR) measured in nominal terms is the same for all risky assets in an equilibrium market. Friend, Landskroner and Losq [5, hereafter F-L-L] have recently shown that CAPM measured in nominal terms understates the MPR if an uncertain inflation is expected and if a covariance between the rate of return on the market and the rate of inflation is positive (p. 1287).

Since the early 1970s, it has been realized that rapid economic development in developing countries leads to an acute inequality in income distribution. To prevent massive dissatisfaction among their citizens, developing countries were urged to achieve economic growth (particularly industrial growth) with distribution of income as their development goal. A good way of promoting growth and dispersal of industrial activities is the establishment of industrial estates in the locations where such activities are desired. This paper formulates the problem of optimal development of industrial estates, with the incorporation of specified minimum levels of development in poverty (priority) sites as distributive targets, as encountered by a Malaysian state government. The linear programming problem so formulated is then shown to be equivalent to a transportation problem, enabling it to be solved and parametically analyzed efficiently. Computational results, obtained using real-life data, show that the subsidy incurred in fulfilling the distributive targets is small compared to the total revenue generated. This justifies the imposition of the distributive targets on the development process. Further, the optimal policy was found to involve decisions to be taken in the initial years of the planning horizon that are fairly insensitive to variation in demand and cost parameters, thereby demonstrating the relative “goodness” of the optimal policy for industrial estate development in the state.

Contrary to Sims's claim [1980], Stogdill [1974] does discuss punishment within organizations, although there is no entry under punishment or punitive per se.

Information evaluation problem, Knowledge, Decision making

Since the early work of Durand (1941), there has been considerable interest in using quantitative models of consumer credit behavior for credit-granting decisions. Most models are based on the concept of “scoring” by use of weights usually determined as statistically significant coefficients of some linear statistical model, frequently the linear discriminant model. It is the purpose of this note, however, to propose maximum likelihood estimation of the logit model as an alternative, and to compare the two models in a “scoring experiment.”

Price controls differ across industries systematically as predicted by the theory regulators maximize political support. This approach views the price controllers as choosing, subject to technology and budget constraints, the appropriate enforcement to balance the support from holding down prices against the opposition from dead-weight loss. Differences in enforcement depend on weights in the official price index, elasticities of supply and demand, and structural characteristics of industries. Data from the 1971-74 regime of price controls are used to test the theory. The empirical evidence clearly shows the predicted differences in price controls for a cross section of industries.