Incentive Alignment in Business Processes via Equilibrium Computation and Penalty Mechanisms

Businesses collaborate to further their own business goals, but may not have each other’s interests in mind. This misalignment of interests can cause issues by necessitating various safeguards to make sure the other party does what is required of them. Such safeguards can be expensive to enforce, or may completely fail if not implemented properly. As business collaboration and the automation of such becomes more integral to society, so does the need to make sure such collaboration is as smooth as possible.
This thesis explores the application of game theory to business process management in order to improve collaboration between organizations. It addresses the inherent inefficiencies in interorganizational business processes arising from misaligned incentives and proposes a novel approach that aligns these incentives through game-theoretic analysis. The work demonstrates that when it is accurate to model business collaborations as perfect information games, it is possible to identify misaligned incentives and realign them, resulting in greater profits for all. Furthermore, the game-theoretic analysis can be done using secure multi-party computation, thereby preserving private business information which may be necessary for the analysis.
In addition, the work presents algorithms for computing Nash equilibria which are essential for game-theoretic analysis. In particular, the work presents algorithms for computing pure strategy Nash equilibria for two-player zero-sum games using fewer comparisons than previously possible. This is a step towards applying the work to business processes which cannot be modeled as perfect information games.
The thesis includes four main contributions: (1) a method for aligning incentives in business processes using game theory and secure multi-party computation; (2) a deterministic algorithm that finds strict pure strategy Nash equilibria in zero-sum games in near linear time in the decision space; (3) a randomized algorithm that finds such pure strategy Nash equilibria in optimal linear time; and (4) an adaptive algorithm that adjusts to the complexity of instances for finding all pure strategy Nash equilibria in a matrix. These results not only enhance the computational feasibility of applying game-theoretic models in practice but also lay the groundwork for scalable, privacy-preserving incentive mechanisms in business process management.
The work bridges business process management and algorithmic game theory, contributing to both fields by demonstrating how theoretical models can be implemented to foster more efficient inter-organizational collaboration.