Incentivizing Honesty in Two-Sided Information Markets: An Evolutionary Game Approach to Crowdsourcing Trust

Ensuring trustworthy participation in decentralized crowdsourcing platforms is a key challenge in information systems research, where strategic heterogeneity, bounded rationality, and lack of centralized enforcement limit classical equilibrium approaches. This study proposes a bi-population game-theoretic framework with state-dependent payoffs, where reputation modeled via a sigmoid function of contribution frequency and accuracy directly shapes incentives for interdependent providers and users. This gives rise to a bi-population replicator framework with state-dependent payoffs, where the evolutionary dynamics of each population are driven by reputation-adjusted incentive differentials. We analyze the boundary equilibria of the resulting system and characterize stability through reputation-dependent incentive thresholds. We show analytically that the honest boundary equilibrium (x, y) = (1, 1) is locally asymptotically stable if and only if (β+δ)R>C−ϕ. We also derive the evolutionarily stable strategy (ESS) condition for the honest strategy in the research of Maynard Smith and Price, where the ESS condition depends on the state-dependent payoff structure. We show that the ESS condition for the honest strategy is progressively relaxed as the agents accumulate more reputation through honest behavior over time. This gives rise to a novel dynamic stability feedback phenomenon wherein the stability condition for the honest equilibrium becomes progressively relaxed as the agents accumulate more reputation through honest behavior over time. The transient dynamics may exhibit nonlinear adjustment patterns due to the gradual evolution of reputation variables, characterized by nonlinear transient adjustment patterns consistent with findings in eco-evolutionary and environmental-feedback modeling. Simulation results, which are based on fifty independent simulation runs with varying initial conditions (x(0) = y(0) ∈ {0.3, 0.5, 0.8}), support the convergence behavior toward the honest equilibrium for the default parameter setup. The sensitivity analysis suggests a non-monotonic performance profile for varying honesty levels γ, which implies an optimal range for γ ∈ [0.55, 0.70]. The main factor for equilibrium honesty is identified as the direct dishonesty penalty φ. As a conclusion, this model provides valuable insights for the design of incentive-compatible crowdsourcing mechanisms, which are supported by analytical results. Future work will focus on empirical validation.