Non-Parametric Stakeholder Discovery: A Process for Mitigating Risk Governance Deficits through Open-Ended Protocols

Abstract

This chapter provides a stakeholder discovery model for distributed risk governance suitable to machine learning and decision-theoretic planning. Distributed risk governance concerns when the underlying risk is not localized or has unknown locality so that any initial interaction with stakeholders is limited and educational and participatory initiatives are costly. Therefore, expecting the initial reaction to communications is critical. To capture this initial reaction, the authors sample the population of potential stakeholders to discover both their concerns and knowledge while handling inaccuracies and contradictions. This chapter provides a stakeholder discovery model that can accommodate these inconsistencies. Stakeholder discovery provides a timely strategic assessment of the risk situation. This assessment forecasts projected stakeholder actions to find if those actions are in line with their strategic interests or if there are better choices using reinforcement learning. Unlike other reinforcement learning formulations, it does not take the state space, criteria, potential observations, other agents, actions, or rewards for granted, but discovers these factors non-parametrically. Overall, this chapter introduces machine learning researchers and risk governance professionals to the compatibility between non-parametric models and early-stage stakeholder discovery problems and addresses widely known biases and deficits within risk governance and intelligence practices.