Strategic resource allocation: an agent-based model of ambidextrous strategies in the pharmaceutical industry

In this study, we investigate how pharmaceutical firms manage the allocation of their resources between exploitation and exploration based on their competitive standing for the attainment of strategic ambidexterity. The dynamic balance between exploration and exploitation is modeled through iterative agent learning and adaptation, where each firm continuously updates its strategic allocation based on competitors’ performance and market feedback. A data-calibrated agent-based model (ABM) is developed for the simulation of resource-allocation dynamics, incorporating Miles and Snow Typology, Porter’s Five Forces, and Ambidexterity Theory. In distinction to previous studies, which were mostly static and regression-based, our current model is empirically calibrated using data from eight top Iranian firms from 2016 to 2023 and, hence, derives firm behaviors directly out of evidence for behavioral validity and against theoretical bias. Porter’s Five Forces are quantified within the framework of the Analytic Hierarchy Process (AHP), and model robustness is confirmed through calibration via a genetic algorithm and validation by Monte Carlo simulation. Results show that investments in exploration accelerate market-share growth and enhance adaptability. Sensitivity analyses demonstrate the managerial implications of learning rates and observation windows. The findings show how a behavioral estimation-based decision model may connect strategy theory with practical application, thereby constituting an empirically backed framework for adaptive allocation in fast-changing market environments such as the healthcare sector.