Search Heuristics Under Multiple Objectives: The Case of Corporate Social Responsibility

Organizations increasingly pursue multiple objectives, yet we know little about how boundedly rational firms search for better strategies when performance is multidimensional. We use corporate social responsibility (CSR) -- pursuing social and financial outcomes simultaneously -- as a motivating context. We distinguish multi-objective decision making (choosing among known alternatives) from multi-objective search (discovering alternatives through path-dependent local moves), and argue that preferences matter through the heuristics that implement them during search. In a dual-landscape NK simulation, we compare five prominent heuristics: Maximize (improve financial performance only), Combine (raise the sum of financial and social performance), Alternate (pursue one goal until stuck, then switch), Penalize (maximize financial performance while deducting shortfalls below a social threshold), and Satisfice (prioritize financial performance only after meeting a social threshold). Across varying complexity, goal correlation, and thresholds, these heuristics produce systematically different trajectories and joint outcomes. Importantly, by escaping local financial optima, Alternate often discovers "oblique" strategies that match or exceed the financial performance achieved by profit-only local search while also improving social performance. We conclude that implementing the same multi-goal preferences through different search heuristics steers firms toward different regions of the social-financial frontier, shaping both the compromises they reach and the opportunities they discover.