Decentralized Task Assignment and Centralized Contracting: On the Optimal Allocation of Authority

2013 ◽  
Vol 26 (1) ◽  
pp. 33-55 ◽  
Author(s):  
Steffen Reichmann ◽  
Anna Rohlfing-Bastian

ABSTRACT To provide efficient incentives, the three components of an incentive system (i.e., performance measurement, rewards, and the allocation of decision rights) need to be balanced against each other. In practice, the authority to decide on these components is frequently distributed across hierarchical levels, thus requiring adjusting centralized decisions with regard to decentralized authority. This paper investigates the centralized design of incentive contracts when decision authority, with respect to the allocation of tasks, is delegated to lower hierarchical levels. It provides an analysis of the optimal allocation of authority (i.e., “who should be the boss”) and considers the interdependencies between organizational design choices and the design of optimal incentives.

2019 ◽  
Vol 95 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Tim Baldenius ◽  
Beatrice Michaeli

ABSTRACT We consider the optimal allocation of decision rights over noncontractible specific investments. Risk-averse business unit managers each engage in general (stand-alone) operations and invest in joint projects that benefit their own and other divisions. Which of the managers should have the authority to choose these investments? With scalable investments, we show that decision rights should be bundled in the hands of the manager facing the more volatile environment. With discrete (lumpy) investments, on the other hand, decision rights should be split between the managers, provided they face comparable levels of uncertainty in their general operations. Splitting decision rights better leverages the inherent investment complementarity, counter to conventional wisdom. Our model generates empirical predictions for the equilibrium association of organizational structure and managers' incentive contracts: bundling of decision rights results in pay-performance sensitivity (PPS) divergence across divisions; splitting them results in PPS convergence. JEL Classifications: M41; D23; D86.


2016 ◽  
Vol 32 (4) ◽  
pp. 1199-1216
Author(s):  
Hela Chakroun ◽  
Mehdi Nekhili ◽  
Tawhid Chtioui

This study investigates complementarities between components of the organizational design of franchising networks. We examine three components of governance, rarely distinguished as such in previous studies involving franchising: allocation of decision rights, performance measurement and incentives. We also analyze interdependencies between these variables. In particular, interdependencies seem more evident between the incentive system and the allocation of decision rights to franchisees, and between the incentive system and performance measurement. We also provide evidence of the role of three franchisee characteristics in franchising’s organizational design: multi-unit ownership, age of the relationship, and geographic distance. Implications for chain management are provided.


2001 ◽  
Vol 91 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Susan Athey ◽  
John Roberts

2017 ◽  
Vol 10 (17) ◽  
pp. 129-146
Author(s):  
Martin Krause

Corporate governance focuses its attention on the structure of the firm and the allocation of decision rights between owners and managers basically, plus other stakeholders. The field has developed extensively during the last decades inspiring reforms and practices as well as learning from them. Most of the analysis though takes into consideration the XXth Century firm, rightfully so since CG is a very practical field in the overlapping map of law, economics and finance. The firm has probably been one of the most successful institutional innovations of the last centuries. Five hundred years ago only a few of them existed, today they are pervasive. Nevertheless, we cannot expect the firm to be the same a hundred years from now as it is today. And if companies are going to be different, how will their corporate governance be affected? The present article does not expect to give an answer to such question. It only attempts to provoke debate and speculation about a possible evolution of the firm based on one single aspect of change: the increased use of dispersed knowledge. After suggesting some development and analyzing present innovations in that direction, we will open up to consideration how those potential changes may affect corporate governance. Of course, there are no specific conclusions, just a call to open our minds to future possible scenarios.


Author(s):  
Youssef Hami ◽  
Chakir Loqman

This research is an optimal allocation of tasks to processors in order to minimize the total costs of execution and communication. This problem is called the Task Assignment Problem (TAP) with nonuniform communication costs. To solve the latter, the first step concerns the formulation of the problem by an equivalent zero-one quadratic program with a convex objective function using a convexification technique, based on the smallest eigenvalue. The second step concerns the application of the Continuous Hopfield Network (CHN) to solve the obtained problem. The calculation results are presented for the instances from the literature, compared to solutions obtained both the CPLEX solver and by the heuristic genetic algorithm, and show an improvement in the results obtained by applying only the CHN algorithm. We can see that the proposed approach evaluates the efficiency of the theoretical results and achieves the optimal solutions in a short calculation time.


2020 ◽  
Vol 110 ◽  
pp. 80-84
Author(s):  
Susan C. Athey ◽  
Kevin A. Bryan ◽  
Joshua S. Gans

The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI's more aligned choice with the need to motivate the human agent to expend effort in learning choice payoffs. When agent effort is desired, it is shown that the principal is more likely to give that agent decision authority, reduce investment in AI reliability, and adopt an AI that may be biased. Organizational design considerations are likely to have an impact on how AIs are trained.


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