Organizational form with behavioral agents

The first chapter discusses how optimal incentive schemes should be set, when agents face limited cognitive capacities and react intuitively to complex incentive contracts. Following Arrows claim that “the scarcity of information-handling ability is an essential feature for the understanding of both individual and organizational behavior”(Arrow (1974), p.37), I analyze how increasing complexity can reverse optimal incentives into bad incentives. Agents face a multitude of different tasks in modern jobs. The increase in complexity is facilitated through new technological possibilities and a shift to flatter hierarchies. Standard contract theory predicts complex contracts, however, actual contracts are simpler. In order to explain this puzzle I propose a model in which agent’s limited attention leads to an instinctive focus on tasks with high outcome variation. Therefore agents end up exerting too much effort in those tasks. This provides an explanation of findings in field studies, where the reduction of incentives increases overall productivity or the introduction of new performance measures has negative effects on some tasks. The second chapter is methodological in the sense that it provides an experimental test for a widely used psychological model, Prospect Theory. We are interested in the role Prospect Theory plays for dynamic inconsistent behavior of decision makers. For Prospect Theory decision makers, there are massive spillovers between sequential choices. The outcomes of early decisions determine whether the decision makers face their subsequent choices from the gain or loss domain, which influences their preferences toward risk. So when it comes to early decisions, their choices can be driven by the anticipations of their own reactions to these domain shifts. We experimentally investigate the quality of Prospect Theory anticipations for a student subject pool and how individual differences in these anticipations are driven by the subjects’ cognitive ability, personality, demographics, and overall stability of Prospect Theory behavior. We find evidence that loss aversion drives dynamic inconsistent behavior. Contrary to our predictions individuals with higher cognitive reflection are not better planners. Businesses can exploit dynamic inconsistencies by offering sequential lotteries in form of products, e.g. trading card games, sticker albums, fantasy football leagues. In determining the role of Prospect Theory in dynamic inconsistent behavior we provide a angle for policy maker and consumer protection, i.e. organizations that protect behavioral agents from exploitation. The third chapter seems to be out of line with the previous two, because it does not assume behavioral agents. Rather we use a different perspective on the question how conflict within organizations, in this case firms, can be reduced through the introduction of a union. Unions reduce the cost of communication. We show that in a relational contracts model with imperfect public monitoring unions can mitigate equilibrium conflict and improve the efficiency of interaction. We modify the standard relational contracts model by assuming asymmetric information regarding the state of the world. Specifically, we assume there are some states of the world in which the firm is hit by an adverse shock, unobservable to the worker, and cannot honor its payment promises. In this situation, the firm always has an incentive to claim that it was hit by the adverse shock and to renege on its promises. We characterize an equilibrium that has periods of cooperation (high effort and bonus payment) and conflict (low effort and no bonus payments) along the quilibrium path. Though in equilibrium there is always truthful revelation of the state of the world, the conflict phases are needed to sustain cooperation. Unions help to communicate the state of the world and to reduce conflicts. Thus, we are able to provide a reason for an organization within an organization that helps to reduce conflicts even with purely self interested agents.