In this chapter, we introduce a coherent framework for analyzing small group dynamics. We first discuss three leading models of group decision-making dynamics: Groupthink, Polythink, and Con-Div. We show how analysts can determine group decision-making dynamics by identifying specific symptoms across a continuum of possibilities. We provide three cases on foreign policy decisions made during the Obama administrations to explain and illustrate the application of these models. We also expand the continuum concept by adding several models of group dynamics, which we call: quasi-Groupthink, quasi-Polythink, quasi-Con-Div.

This chapter examines the effect of group dynamics on the 2016 decision within the administration of President Barack Obama to lead the international coalition in a mission to liberate Raqqa, Syria, from the Islamic State. The authors show that whereas the groupthink syndrome characterized the decision-making process of the US-led coalition’s decision to attack Raqqa, it was polythink that characterized the decision-making dynamics both in the US-led coalition and within the inner circle of Obama’s own foreign policy advisors. Through case-study analysis, the authors illustrate that groupthink is more likely in strategic decisions, whereas polythink is more likely in tactical decisions.

Modern AI systems achieve remarkable performance through fundamentally stochastic processes—machine learning models that function as high-dimensional probability density functions, outputting the most likely predictions given training data. While these systems can match or exceed human performance on average, their methodology produces fundamentally different failure modes than human reasoning, leading to errors that appear nonsensical from a human perspective but are predictable given their probabilistic nature. This has critical implications for high-consequence environments such as military applications where decisions cannot be reversed and may affect lives and material assets definitively. Through detailed analysis of contemporary AI’s working mechanisms—particularly how knowledge is acquired through statistical pattern recognition rather than causal reasoning—this paper demonstrates why AI systems inherit biases, cannot distinguish plausibility from factual correctness, and exhibit confident behaviour even when wrong. Written to provide guidance for non-technical stakeholders, specifically but not exclusively in the military domain, it posits that for effective deployment of AI in high-consequence scenarios, processes need to be implemented that make sure all human stakeholders are aware of these facts, develop adequate scepticism of the AI system, and remain actively involved in the decision-making. For military applications specifically, this understanding reveals that effective human-AI collaboration requires more than oversight: it demands co-learning frameworks that maintain meaningful human control through bidirectional information flow, and behavioural and functional awareness on the human side. We give an outlook to decentralized, co-learned AI system tailored to specific teams in dedicated co-learning labs to mitigate power concentration risks while preserving essential human capacities, including moral judgment to exercise mercy.

The Cambridge Handbook of Political Psychology - February 2022