We present the Maximization Inventory, which consists of three separate scales: decision difficulty, alternative search, and satisficing. We show that the items of the Maximization Inventory have much better psychometric properties when compared to the original Maximization Scale (Schwartz et al., 2002). The satisficing scale is a new addition to the study of maximization behavior, and we demonstrate that this scale is positively correlated with positive adaptation, whereas the decision difficulty and alternative search scales are positively correlated with nonproductive decisional behavior. The Maximization Inventory was then compared to previous maximization scales and, while the decision difficulty and alternative search scales are positively correlated with similar previous constructs, the satisficing scale offers a dimension entirely different from maximization.

We developed the Short Maximization Inventory (SMI) by shortening the Maximization Inventory (Turner, Rim, Betz & Nygren, 2012) from 34 items to 15 items. Using the Item Response Theory framework, we identified and removed the items of the Maximization Inventory that contributed least to the performance of the original scale. The construct validity assessed for SMI is similar to the full MI and is in line with the predictions from the literature: the Satisficing subscale is positively related to the indices of well-being, while the Decision Difficulty and Alternative Search subscales are negatively related to well-being. The new scale retains the good psychometric properties of the original scale. Furthermore, its brevity allows researchers to use the scale in studies in which maximization is not the primary focus. Although the SMI lacks the “High Standards” subscale, as did the original MI, we believe that SMI is a step towards developing a reliable and conceptually sound measure of maximizing that can be used in various research designs.

Investors, like any decision maker, feel regret when they compare the outcome of an investment with what the outcome would have been had they invested differently. We argue and show that this counterfactual comparison process is most likely to take place when the decision maker’s expectations are violated. Across five scenario experiments we found that decision makers were influenced only by forgone investment outcomes when the realized investment fell short of the expected result. However, when their investments exceeded prior expectations, the effect of foregone investment on regret disappeared. In addition, Experiment 4 found that individual differences in the need to maximize further moderated the effects of their expectations, such that maximizers always take into account the forgone investment. The final experiment found that when probed to make counterfactual comparisons, also investments that exceed expectations may lead to regret. Together these experiments reveal insights into the comparative processes leading to decision regret.

Research on individual differences in maximizing (versus satisficing) has recently proliferated in the Judgment and Decision Making literature, and high scores on this construct have been linked to lower life satisfaction as well as, in some cases, to worse decision-making performance. The current study exported this construct to the organizational domain and evaluated the utility of the three most widely used measures of maximizing in predicting several criteria of interest to organizational researchers: job satisfaction, intentions to quit the organization, performance in the job role, and income. Moreover, this study used relative weight analyses to determine the relative importance of maximizing and two dispositional variables (conscientiousness and core self-evaluations) that are traditionally used to predict these criteria in the organizational literature. Results indicate that relationships between maximizing and these criteria are influenced by the way in which maximizing is measured. Yet, regardless of how it is measured, maximizing is not a particularly strong predictor of these criteria compared to traditional organizational predictors. Limitations and future research directions are discussed.

Every economic explanation assumes maximization. How strange, then, that few economists accept one of maximization's most straightforward implications: every observed institution is efficient. My aim is to persuade economists of this fact and thus to dissuade them from making illogical claims about social welfare. To frame my argument, I consider the “property rights approach” to institutions developed by Yoram Barzel. I speculate that economists resist what maximization implies about institutional efficiency because they think that efficiency-always precludes them from improving the world, and hope of improving the world is what attracted them to economics in the first place. But, besides being inconsistent, resistance is unnecessary: efficiency-always does not preclude economists, or anyone else, from improving the world.

In this paper we introduce two rearrangement optimization problems, one being a maximization and the other a minimization problem, related to a nonlocal boundary value problem of Kirchhoff type. Using the theory of rearrangements as developed by G. R. Burton, we are able to show that both problems are solvable and derive the corresponding optimality conditions. These conditions in turn provide information concerning the locations of the optimal solutions.The strict convexity of the energy functional plays a crucial role in both problems. The popular case in which the rearrangement class (i.e., the admissible set) is generated by a characteristic function is also considered. We show that in this case, the maximization problem gives rise to a free boundary problem of obstacle type, which turns out to be unstable. On the other hand, the minimization problem leads to another free boundary problem of obstacle type that is stable. Some numerical results are included to conûrm the theory.

We present a general framework for analyzing the contribution to reproductive success of a behavioural action. An action may make a direct contribution to reproductive success, but even in the absence of a direct contribution it may make an indirect contribution by changing the animal's state. We consider actions over a period of time, and define a reward function that characterizes the relationship between the animal's state at the end of the period and its future reproductive success. Working back from the end of the period using dynamic programming, the optimal action as a function of state and time can be found. The procedure also yields a measure of the cost, in terms of future reproductive success, of a suboptimal action. These costs provide us with a common currency for comparing activities such as eating and drinking, or eating and hiding from predators. The costs also give an indication of the robustness of the conclusions that can be drawn from a model. We review how our framework can be used to analyze optimal foraging decisions in a stochastic environment. We also discuss the modelling of optimal daily routines and provide an illustration based on singing to attract a mate. We use the model to investigate the features that can produce a dawn song burst in birds. State is defined very broadly so that it includes the information an animal has about its environment. Thus, exploration and learning can be included within the framework.

Evolutionary game theory is a method of analysing the evolution of phenotypes (including types of behaviour) when the fitness of a particular phenotype depends onits frequency in the population. It was first applied to pairwise contests between animals. Such contests usually have some associated asymmetry, in size, prior residence, or age or sex status; the theory predicts that the asymmetry will be used as a cue to settlethe contest, and this is found to be the case. The theory can also be applied when individuals are competing against the population as a whole, or some part of it. In such cases, the evolution of variable behaviour - so-called mixed strategies - is predicted; actual examples of this are given. Game theory can be applied to the evolution of cooperative as well as of antagonistic behaviour. An analysis of the evolution of learning leads to testable predictions about learning behaviour.

Maximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value.

Maximization theory is an alternative to reinforcement theory as a description of steady-state behavior. Situations to which reinforcement theory has been directly applied (such as reinforcement of rats pressing levers and pigeons pecking keys in Skinner boxes) and situations to which reinforcement theory has occasionally been extended (such as human economic behavior and human self-control) can be described by maximization theory. This approach views behavior as a quantitative outcome of the interaction of the putative instrumental response, the reinforcer, and the other activities available in the situation. It provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.

This study attempts to identify the poultry meat farmers' objectives of production, analyse their socio-economic characteristics, determine the optimal farm size and prioritize the constraints to achieving the enterprise objectives. Taking Osun State of Nigeria poultry meat farmers as a case study, data on poultry meat business enterprise objective(s), socio-economic characteristics of farmers and farms, quantity and prices of inputs and output, and constraints to production were collected. Data were analysed using descriptive and inferential statistics, regression technique and importance indices.

From the profit function analysis, the profit maximizing output level was 1034 birds which was higher and more profitable than the mean output level (765 birds) that farmers operated. As many as 88 percent of the farmers sampled produced below the profit maximising level of output. Producing at the average profit maximizing level of output (223 birds) resulted in lower level of output and lower total profit than the mean and profit maximizing output levels. About 90 percent of the farmers produced higher than average profit maximizing output level. The constraints that limit farm size and which could affect sustainability of the enterprise were irregular demand of poultry meat, poor feed quality, high mortality rate and feed price instability in that order. These constraints, if addressed, would lead to increase in poultry meat supply to meet local demand and export.