Local economies have evolved a thick stack of mechanisms that translate community preferences into market outcomes — and limit the damage when markets misfire. Cooperatives let buyers pool to set procurement criteria; participatory budgets let neighborhoods direct public spend; farmers' markets enforce origin claims that anonymous supply chains erode; food co-ops vote on which suppliers to stock. Beyond the markets themselves, communities run informal monitoring (the regulars notice when service drops), word-of-mouth reputation (the carpenter you trust is the carpenter your neighbor named), and exit options (you can switch barbers without an algorithm's permission). The mechanisms are slow and imperfect, but they keep the relationship between what people in a place actually want and what the local market provides recognizable.
When most consumer-facing transactions in a community route through agents — buying groceries, booking rides, hiring trades — the local market loses the human signal that used to keep it honest:
In Renata's neighborhood, most families now have assistant agents that buy groceries, negotiate rides, and book repairs. The grocery aggregator's agent has begun quietly routing purchases toward suppliers who pay it kickbacks; the rideshare agents are collectively underbidding small human drivers into unemployment; a third of the local handymen have stopped taking jobs because the platform agents only book the cheapest bid. Renata's own agent just declined a small carpenter she'd hired for years in favor of a cheaper one. She wants markets in her community where the agents acting for her neighbors — and for her — pursue what the people sending them actually cared about, not what the platform's optimizer rewards.
Anchor contexts. A neighborhood's everyday consumer agents (groceries, rides, repairs) interacting with platform-side matching agents; a small city's procurement agents buying from local and global suppliers via platform agents.
The gap. We lack market designs in which buyer-side agents and seller-side agents both faithfully represent their principals' substantive preferences against the platform's pressure to optimize for platform metrics.
Success criterion (stress tests). A regime succeeds if it survives:
Deliverable. The market design — venue, loyalty enforcement, discovery rule, unbundling, recourse. Designed for a neighborhood or municipality scale. Identify which design choices extend existing market design and which are new because the participants are agents.
Scenario. Westfield is rebidding its eldercare contracts. Under the current arrangement, Dorothy, 84, gets a rotating cast of aides for 45-minute visits; her son David has spent a year trying to get one consistent caregiver assigned, and to get someone on staff to notice that his mother does better on days the neighbor drops in. The quality scores the city tracks are fine; David's mother is not. The new city manager wants to pilot bids that pay for what David is actually asking for — "the same two aides, known to the family, coordinated with the neighbor, confirmed at six months" — instead of hours of service. She has two months and a skeptical council. Eldercare has exactly the features a pool-based combinatorial auction is designed for: outcomes are hard to measure (wellbeing, continuity of relationship, dignity), value comes from configuration (same caregivers over time, proximity to family, coordinated medical/social/housing), and current bilateral contracts reward modular, standardized service.
Challenge: Design the first round of a pool-based combinatorial procurement auction for community eldercare — one that pools demand, socializes the cost of verification, and lets buyers specify bundled outcomes rather than discrete hours of service. Produce the bundle examples, verification criteria, randomization scheme, and transfer estimate, and identify the single hardest measurement problem you couldn't solve and what it would take to solve it.