BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces

• URL: http://arxiv.org/abs/2606.02798v1
• Date: Mon, 01 Jun 2026 19:04:36 GMT
• Title: BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces
• Abstract summary: textscBehaviorBench is a benchmark for evaluating personalized decision modeling from real-world behavioral traces.<n>textscBehaviorBench reconstructs wallet-level decision histories from observed public prediction-market and on-chain records.<n>Across 2,000 evaluation wallets, the benchmark contains 141,445 Belief instances and 1,485,972 Trade instances.
• Score: 53.55407626944686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited. Existing benchmarks for user understanding often rely on simulated users or model-generated behavior, even though recent work cautions that model-based simulations can diverge systematically from human behavior. We introduce \textsc{BehaviorBench}, a benchmark for evaluating personalized decision modeling from real-world behavioral traces. \textsc{BehaviorBench} reconstructs wallet-level decision histories from observed public prediction-market and on-chain records, and organizes them into two complementary task layers: \emph{Belief prediction}, which predicts a user's final revealed stance and confidence in a market, and \emph{Trade prediction}, which predicts the direction and amount of individual transactions. Across 2,000 evaluation wallets, the benchmark contains 141,445 Belief instances and 1,485,972 Trade instances, with disjoint support pools for retrieval-based evaluation. We evaluate frontier and open-weight generative models under four history interfaces: no personalization, direct recent history, generated user profiles, and retrieved support-wallet evidence. Personalization improves Belief prediction more consistently than Trade prediction, model rankings change across task layers and metrics, and different history interfaces expose different failure modes. \textsc{BehaviorBench} provides an evaluation setting for studying whether personalized methods can use real-world behavioral evidence rather than simulated users alone.

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