ARC Talk: Daria Gritsenko, Technical University Munich: Bounded Rationality in Political Judgment: Cognitive Effort Reduction in Public Evaluations of Algorithmic Governance

Bounded Rationality in Political Judgment: Cognitive Effort Reduction in Public Evaluations of Algorithmic Governance

How do individuals evaluate the legitimacy of complex, automated state authority? Traditional political science frameworks rely on normative models of comprehensive, weighted information integration across input, throughput, and output cues. However, under the constraints of bounded rationality, citizens must dramatically simplify these multi-attribute demands. This talk presents findings from the ongoing AGAPP project, tracking how citizens reduce cognitive effort when evaluating algorithmic governance. Integrating frameworks of attribute substitution and motivated reasoning, we investigate the cognitive micro-foundations of legitimacy judgments using multi-wave experimental data tracking public evaluations of automated border systems. We explore two related cognitive processes. First, we demonstrate that citizens reduce cognitive effort by substituting intricate systemic architectures with highly accessible, proximal environmental cues. Second, we show how evaluations are filtered through motivated reasoning, where outcome-dependence dictates selective attention and adaptive weighting of process cues. Furthermore, using qualitative responses, we demonstrate how reasoning serves an argumentative function—allowing citizens to construct socially defensive justifications for their governance preferences. By rethinking individual legitimacy judgments as an adaptive response to socio-technical innovations, we provide a fresh framework for exploring the conditions for democratic governance in the age of AI.

Hybrid talk: https://arc.mpib-berlin.mpg.de/public-with-recording/
Meeting-ID: 686 0984 3929
Passcode: 250547