Incentives are an effective mechanism to align the interests of decision-makers. For example, employers use incentives to motivate their employees to take actions that benefit both the employers and the employees. Incentives also play a role in the interaction between firms, for example in a supply chain network. The prevalent approach to analyzing interactions between decision-makers is through principal-agent models. Due to mathematical intractability, the majority of these models are restricted to the interaction between two decision-makers. However, modern organizations have many decision-makers that interact with each other in a network. Therefore, effective incentive mechanisms for systems with many decision-makers (agents) must account for the numerous network interdependencies. The objective of this dissertation is to design incentive mechanisms for systems with many agents, with a focus on teams and multi-firm networks. Methodologically, our approach applies and builds upon multiscale decision theory (MSDT). MSDT can effectively and efficiently model the interdependencies between decision-makers and their optimal response to incentives. This dissertation consists of three parts. The first part focuses on incentives in teams, where multiple subordinates work under a single supervisor. The contribution of the team model to MSDT is the introduction of continuous decision variables; prior MSDT models have only used discrete decision variables. In the second and third part of this dissertation, we analyze a network of collaborating firms in a systems engineering project and focus on verification decisions. We introduce a belief-based model, which is a novel approach for both MSDT and verification modeling in systems engineering. Using MSDT, we determine how incentives can be used by a contractor to motivate a subcontractor to verify its design when the subcontractor prefers not to do so. We extend this two-firm model to a general multi-firm network model for verification coordination and incentivization. This firm network resembles the inter-firm collaboration present in most large-scale system engineering projects. Through better aligned verification activities, system-wide verification costs decrease, while the reliability of the final system improves. / Ph. D. / Incentives are often used to align the interests of decision-makers in modern organizations. Employers use incentives to motivate their employees to take actions that benefit both the employers and the employees. Incentives also play a role in the interaction between multiple firms, for example in a supply chain network. The prevalent approach to analyzing interactions between decision-makers is through the so-called principal-agent models. Due to mathematical intractability, the majority of these models are restricted to the interaction between two decision-makers. However, modern organizations have many decision-makers that interact with each other in a network. Therefore, effective incentive mechanisms for systems with many decision-makers (agents) must account for the numerous interdependencies that arise due to the organizational structure. The objective of this dissertation is to design incentive mechanisms for systems with many agents, with a focus on teams and multi-firm networks. Methodologically, our approach applies and builds upon multiscale decision theory (MSDT). MSDT can effectively and efficiently model the interdependencies between decision-makers and derive optimal organization-wide incentives. This dissertation consists of three parts. The first part focuses on incentives in teams, where multiple employees work under a single manager. The contribution of the team model to MSDT is the introduction of continuous decision variables; prior MSDT models have only used discrete decision variables. In the second and third part of this dissertation, we analyze a network of collaborating contractors in a systems engineering project and focus on design verification strategies. We introduce a belief-based model, which is a novel approach for both MSDT and verification modeling in systems engineering. Using MSDT, we determine how incentives can be used by a contractor to motivate a subcontractor to verify its design when the subcontractor prefers not to do so. We extend this two-firm model to a general multi-firm network model for verification coordination and incentivization. This firm network resembles the inter-firm collaboration present in most large-scale system engineering projects. Through better aligned verification activities, system-wide verification costs decrease, while the reliability of the final system improves.

Financial incentives offered by payers to health care providers and patients have been identified as a key mechanism to lower costs while improving quality of care. How to effectively design incentive programs that can align the varying objectives of health care stakeholders, as well as predict programs' performance and stakeholders' decision response is an unresolved research challenge. The objective of this study is to establish a novel approach based on multiscale decision theory (MSDT) that can effectively model and efficiently analyze such incentive programs, and the complex health care system in general. The MSDT model captures the interdependencies of stakeholders, their decision processes, uncertainties, and how incentives impact decisions and outcomes at the payer, hospital, physician, and patient level. In the first part of this thesis, we study the decision processes of agents pertaining to the investment and utilization of imaging technologies. We analyze the payer-hospital-physician relationships and later extend the model to include radiologist and patient as major stakeholders in the second part of this thesis. We focus on a specific incentive program, the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs). The multi-level interactions between agents are mathematically formulated as a sequential non-cooperative game. We derive the equilibrium solutions using the subgame perfect Nash equilibrium (SPNE) concept and the backward induction principle, and determine the conditions under which the MSSP incentive leads to the desired outcomes of cost reduction and quality of care improvements. In the third part of this thesis, we study the multi-level decision making in chronic disease management. We model and analyze patients' and physicians' decision processes as a general-sum stochastic game with perfect information and switching control structure. We incorporate the Health Belief Model (HBM) as the theoretical foundation to capture the behavioral aspect of agents. We analyze how incentives and interdependencies affect patients' engagement in health-promoting activities and physicians' delivery of primary care services. We show that a re-alignment of incentives can improve the effectiveness of chronic disease management. / Ph. D.