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The process of acquiring large-scale complex systems is usually characterized by cost and schedule overruns. We develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. Specifically, we cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a contract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. The objective of this paper is to study how different parameters in the acquisition procedure affect the bidders' behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior. Also, we study the effects of different contract types on the winner's optimal effort level necessary to meet the system requirements. We run exhaustive numerical simulations, which show that cost-plus contracts are particularly prone to strategic misrepresentation. This analysis can be expanded to help the government select procedures that achieve specific goals, such us minimizing cost overruns.

Understanding decision making in design is becoming increasingly important within systems engineering and design research. While controlled experiments are used for testing hypotheses and are playing a significant role in developing theories, there is a lack of support for capturing and reusing knowledge associated with experimental design. The goal of this paper is to model the information required for designing and executing experiments, thereby enabling the reuse of past experimental designs. Using declarative formulations of four aspects of an experiment, namely, problem, process, participants and incentives, we extract the generic elements and standardize the structure of information as a Decision Experiment Design Support (DEDS) template, based on which an ontology is developed. The information necessary to execute an experiment is archived as DEDS templates and represented by a frame-based ontology that can be reused from one experiment to another. The approach is illustrated using two example experiments for studying the decision strategies of information acquisition, and the impact of domain knowledge on decisions. This paper offers a fundamental step towards reducing the barriers in designing and executing behavioral experiments for design and systems engineering research.