UCLA

Decision-makers have access to better environmental information through tools like life-cycle assessment (LCA). However, these methods often implicitly assume that decision-makers make rational assessments when weighing environmental criteria. The impacts of behavioral biases and context effects on decisions remain less recognized, despite being well-documented in behavioral science. Current sustainability approaches offer little guidance in finding optimal solutions when decision-makers' preferences are unknown. Unlike traditional trade-offs involving economic factors, sustainability trade-offs involve intangible and emotionally charged dimensions. Firms aiming for sustainability lack clear guidelines for trading-off environmental and social impacts. This dissertation seeks to assist decision-makers in the context of sustainability from behavioral and optimization-based perspectives.We first conduct an experiment to test whether decision makers are subjected to two context effects, namely attraction and compromise effects, when faced with environmental trade-offs. Our results show that these context effects are prevalent and substantial in both environmental and non-environmental settings, which highlights the need to integrate behavioral science into environmental decision-making. We introduce an interactive optimization method that aims to help decision makers with difficult trade-offs when their value function is not known. This method involves asking decision makers pairwise comparison questions to identify the optimal solution. The approach minimizes the cognitive burden on decision makers by asking fewer, easier questions while still guaranteeing an optimal solution. We test the method in the context of sustainable sourcing in the apparel industry, demonstrating its effectiveness in converging to optimal solutions with fewer decision-making steps compared to traditional methods. Initial feedback from industry practitioners is promising. An interactive approach like this is uncommon in decision-making in sustainable operations. A more conventional approach would be to elicit the decision-maker's weights and then use a traditional optimization method using those weights. To test which method decision-makers prefer, we also develop an experimental framework, using oTree, to compare the performance of the proposed interactive optimization method with a more traditional approach, eliciting weights through direct rating and then optimizing. Although the experimental framework is initially designed specifically to assess our interactive algorithm, the framework highlights more generally how experiments can be designed that combine Gurobi-based optimization within the experimental environment provided by oTree.