arXiv subjects

Ayato Kitadai

Publications and source records attributed to Ayato Kitadai.

5 recordsLinked to original sources

arXiv2026

Do Matching Mechanisms Work with LLM Agents?

This study examines whether standard matching mechanisms function as intended in LLM-agent markets, where LLM agents make allocation-related decisions as delegated decision-makers. We compare decentralized free-negotiation markets with centralized mechanism-based markets including several representative mechanisms. Across controlled one-to-one matching environments, mechanism-based markets generally outperform free negotiation in terms of stability and efficiency. We also find that LLM agents report preferences truthfully at substantially higher rates than human subjects in comparable DA and EADA environments. However, truth-telling is not uniformly aligned with formal strategy-proofness across all mechanisms: TTC, despite being strategy-proof, does not always elicit higher truth-telling than EADA. These results suggest that matching theory provides a useful but incomplete guide for designing institutions in LLM-agent markets.

cs.GT↗

arXiv2026

Decision Support System for Technology Opportunity Discovery: An Application of the Schwartz Theory of Basic Values

Discovering technology opportunities (TOD) remains a critical challenge for innovation management, especially in early-stage development where consumer needs are often unclear. Existing methods frequently fail to systematically incorporate end-user perspectives, resulting in a misalignment between technological potentials and market relevance. This study proposes a novel decision support framework that bridges this gap by linking technological feasibility with fundamental human values. The framework integrates two distinct lenses: the engineering-based Technology Readiness Levels (TRL) and Schwartz's theory of basic human values. By combining these, the approach enables a structured exploration of how emerging technologies may satisfy diverse user motivations. To illustrate the framework's feasibility and insight potential, we conducted exploratory workshops with general consumers and internal experts at Sony Computer Science Laboratories, Inc., analyzing four real-world technologies (two commercial successes and two failures). Two consistent patterns emerged: (1) internal experts identified a wider value landscape than consumers (vision gap), and (2) successful technologies exhibited a broader range of associated human values (value breadth), suggesting strategic foresight may underpin market success. This study contributes both a practical tool for early-stage R\&D decision-making and a theoretical link between value theory and innovation outcomes. While exploratory in scope, the findings highlight the promise of value-centric evaluation as a foundation for more human-centered technology opportunity discovery.

cs.HC↗

arXiv2026

"Sail Fast, Then Wait" in First-come, First-served Port Queues: Information Sharing for Sustainable Shipping

This study develops a novel class of queueing game to explain a common practice in cargo shipping "Sail Fast, Then Wait" (SFTW), and demonstrates that resolving information asymmetry among ships can deconcentrate port arrival times. We formulate a competitive navigating environment as an incomplete information game where players strategically decide their arrival time within heterogeneous feasible sets under First-Come, First-Served port policy. Our results show that in incomplete information settings, SFTW emerges as the unique symmetric equilibrium. Conversely, under complete information, the set of equilibria expands, allowing for slower and more environmentally friendly actions without compromising service order. We further quantitatively evaluate the effect of information enrichment based on empirical data. Our findings suggest that the prevalence of technologies enabling ships to infer others' private information can effectively reduce SFTW and enable more energy-efficient and environmentally sustainable operations.

econ.TH↗

arXiv2025

Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics

Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data from behavioral economics to adjust model biases. Applying this method to the ultimatum game--a standard but difficult benchmark for LLMs--we observe improved alignment between simulated and empirical behavior, particularly on the responder side. While further refinement of trait representations is needed, our results demonstrate the promise of persona-conditioned LLMs for simulating human-like decision patterns at scale.

cs.GT↗

arXiv2024

Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?

Economic experiments offer a controlled setting for researchers to observe human decision-making and test diverse theories and hypotheses; however, substantial costs and efforts are incurred to gather many individuals as experimental participants. To address this, with the development of large language models (LLMs), some researchers have recently attempted to develop simulated economic experiments using LLMs-driven agents, called generative agents. If generative agents can replicate human-like decision-making in economic experiments, the cost problem of economic experiments can be alleviated. However, such a simulation framework has not been yet established. Considering the previous research and the current evolutionary stage of LLMs, this study focuses on the reasoning ability of generative agents as a key factor toward establishing a framework for such a new methodology. A multi-agent simulation, designed to improve the reasoning ability of generative agents through prompting methods, was developed to reproduce the result of an actual economic experiment on the ultimatum game. The results demonstrated that the higher the reasoning ability of the agents, the closer the results were to the theoretical solution than to the real experimental result. The results also suggest that setting the personas of the generative agents may be important for reproducing the results of real economic experiments. These findings are valuable for the future definition of a framework for replacing human participants with generative agents in economic experiments when LLMs are further developed.

cs.GT↗