Arijit Mukherji

Activity-Based Costing for Economic Value Added

Economic value added, which is the currently popular term for the traditional accounting concept of residual income (RI), subtracts from operating income an interest charge for invested capital. This paper provides an activity-based cost system that supports RI maximization. We construct a model of participative budgeting for a multi-activity firm in which the cost system allocates plant and equipment cost to products using a formula that includes the interest charge. The budget system we design enables decision makers to identify RI-improving opportunities for outsourcing and dropping unprofitable products. The budget system also has the “open-architecture” property that additional informal communication among activity managers can only serve to increase RI.

The Asymptotic Optimality of Residual Income Maximization

Residual income subtracts from operating income an interest charge for invested capital. Residual income can be calculated each period from current accounting information, unlike discounted cash flow (DCF), which requires the knowledge of future cash flows. This paper provides a normative justification for residual-income maximization by showing that if investment decisions are made myopically each period to maximize residual income, the resulting path asymptotically maximizes discounted cash flow. Thus, under the assumptions of the model, residual-income maximization is a heuristic that leads to the long-run DCF-optimum.

Moral hazard and contractibility in investment decisions

Abstract This paper uses a principal-agent framework to study the incentive effects of contractibility and moral hazard at the first stage of a two-stage investment decision. We show that when the intermediate signal is publicly observable and contractible, there is overinvestment (relative to the first-best) in second-stage production, purely for incentive reasons. However, if the intermediate signal cannot be contracted on and, in addition, the agent and the principal are permitted to act opportunistically, the optimal delegated investment decision is to underinvest. We discuss the implications of our analysis for the literature on costly monitoring.

Decentralized problem solving using the double auction market institution

Abstract Decentralized decision making is an important problem in distributed artificial intelligence (DAI) and multi-agent systems (MAS). Given a multi-agent system, the actions or choices made by one agent can affect the actions or choices that can be made by other agents. Wellman (Artificial Intelligence Research, 1, 1–23, 1993) has recently proposed the approach of market-oriented programming for decentralized decision making. By transforming the decentralized decision making problem into a computational economy, market-oriented programming draws on the theory of general equilibrium to establish the existence of a competitive equilibrium. The competitive equilibrium of the computational economy represents the market solution to the original problem. General equilibrium theory, however, is institution free and provides no information about the dynamic process by which the competitive equilibrium is found. Wellman uses a variant of the market institution known as tâtonnements. Tâtonnements requirement of strict synchronization of the individual agents restricts market-oriented programming to purely atemporal situations (problems that have a stationary environment and hence a stationary equilibrium). In this paper we suggest the use of the continuous double auction as a general framework for market-oriented programming. By using the continuous double auction, market-oriented programming can also be applied to problems where the environment, and therefore the equilibrium, is continuously evolving with time.

Designing an Incentive-Compatible Contract

One of the most important problems in modern economics is concerned with the interaction of incentives, information, and economic mechanisms that solve them. This is typically posed as the problem of designing an optimal mechanism using game theory. The solution concept that is used is that of Bayesian-Nash equilibrium (or refinements of that concept).