Bio-Economic Hedging against Algorithmic Shocks: A Predictive Framework for Autonomous Consumer Stabilization in Regional Markets

Abstract The advent of viral short-form video trends on digital media platforms generates sudden, non-linear demand shocks in agri-food markets, causing extreme price volatility that harms both smallholder family farmers and local consumers. This study introduces a mathematical stochastic optimization framework and predictive hedging strategies designed to stabilize regional supply chains before consumption spikes cascade into retail networks. Acknowledging the low technological adoption capacity among smallholder producers, this paper proposes a centralized governance model wherein predictive tracking is operationalized by regional producer organizations and protective consortia. These intermediary entities transform macro-level algorithmic signals into simplified, actionable logistics and harvesting directives for individual farmers. Theoretical simulations demonstrate that anticipatory inventory hedging and coordinated baseline pricing significantly minimize unsold surpluses and shield regional economic margins. This study offers crucial insights for agricultural economics and provides an institutional framework for enhancing the resilience of short food supply chains.