A Drift Diffusion Model Approach to Moral Decision-Making: Toward a Computational Framework for Ethical Dilemmas

Can ethical dilemmas be resolved through computational models? In this paper I propose a novel integration of the Drift Diffusion Model (DDM) with a value-based framework for moral reasoning. Based on insights from neuroethics and cognitive neuroscience, I argue that moral decisions —especially under conditions of uncertainty, irreversibility, and emotional interference— can be modeled as processes of noisy evidence accumulation. I introduce the concept of an ethical balance mainly composed of three evaluative variables: sentience, intentionality, and innocence. These inputs are mapped to DDM parameters such as drift rate, decision threshold, and starting point bias. Through illustrative moral scenarios, I show how this framework can both predict and simulate moral judgments. The goal is not to replace normative ethics, but to demonstrate how scientific modeling can enhance our understanding of how moral reasoning unfolds in the brain. This maybe opens the path to a computational ethics grounded in real cognitive processes.