Decentralized Mechanism Design for Self-Selected Goals in Crowdsourcing Systems
Keywords:
mechanism design, crowdsourcing, decentralization, self-selected goals, governance, fairnessAbstract
Crowdsourcing platforms mediate the exchange of labor, knowledge, and creativity at global scale, yet their centralized design concentrates governance in the hands of platform operators, often misaligning the objectives of workers, requesters, and the broader socio-technical ecosystem. This paper explores the conceptual architecture, incentive properties, and systemic implications of decentralized mechanism design that empowers workers to self-select their performance goals. Rather than imposing extrinsic rewards or algorithmic control exclusively from the top down, a decentralized framework embeds goal selection within a transparent, verifiable, and participatory governance layer. We analyze how such self-selected goal mechanisms can be realized through distributed ledger technologies, smart contracts, and reputation-based consensus protocols, and we examine the structural trade-offs that arise around incentive compatibility, collusion resistance, fairness, and quality assurance. The discussion bridges mechanism design theory, empirical findings on gig worker behavior, and emergent practices in decentralized autonomous organizations, highlighting the delicate balance between individual autonomy and system-wide robustness. Special attention is given to deployment challenges, including scalability under dynamic participation, the cost of on-chain computation, and the regulatory uncertainty surrounding algorithmic governance and labor classification. The paper further evaluates the policy dimensions of encoding work norms into immutable protocols and the sustainability of platforms that distribute authority over task allocation and reward distribution. We argue that integrating self-selected goals into decentralized mechanism design constitutes a promising pathway toward more equitable, resilient, and adaptive crowdsourcing infrastructures, but it demands careful institutional design to prevent new forms of exclusion and manipulation. The analysis contributes a forward-looking research agenda at the intersection of distributed systems, algorithmic economics, and platform governance.
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