arXiv subjects

Naoto Imura

Publications and source records attributed to Naoto Imura.

6 recordsLinked to original sources

arXiv2026

How a shared state is described determines whether AI agents synchronize

Language-model agents increasingly act in populations, where the outcome that matters is collective: whether they align, split or fail to coordinate. Each acts not on the world but on a text description of it, a choice usually fixed in software. Using synchronization, the canonical probe of how interaction rules produce collective order, we show that this choice can decide the outcome. Agents on a circle chose to advance, stay or move back after reading the others' relative positions, in 507,112 valid responses across matched populations, controlled inputs and three model families. In GPT, numerical summaries aligned every matched population at both positive couplings, whereas histograms aligned none; Claude showed the reverse at the stronger coupling. Re-describing identical states shifted action probabilities in all three families, even between histograms carrying the same information. No single directional coefficient explained the outcome: state descriptions are part of the interaction rule that turns individual responses into collective order.

physics.soc-ph↗

arXiv2026

When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets

Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight matching market, and which platform design choices contain it. We carried out agent-based simulations in which fifty shipper agents, built on commercial LLMs from OpenAI (GPT), Anthropic (Claude), and Google (Gemini), procure truckload capacity for thirty days. The market implements the rules of digital freight matching: each load is offered down the shipper's ranked list of carriers (waterfall tendering), carriers have daily capacity limits, spot prices respond to congestion, and carrier ratings accumulate with transactions. We found three risks and one remedy that works. Agents converged at once: for a fixed sampled carrier population, the same carrier was the modal first choice of every model on day one, attracting up to 76% of requests. Because each agent picks from its own randomly drawn list of displayed candidates, the platform controls how many options each shipper sees; concentration rose steeply once lists exceeded about ten carriers, with the onset differing across models. Which carriers ended up dominant varied widely from one sampled market to another, and displaying true quality instead of estimated ratings changed neither the level nor this variability (by design, quality affects only what agents see, never delivery outcomes). Against these risks, disclosing each carrier's remaining daily capacity cut concentration by a third and doubled shipper surplus, while vendor diversification, list-order randomization, and popularity display showed no clearly detectable effect. Platform information design, ahead of model choice or model regulation, is the lever that works.

physics.soc-ph↗

arXiv2023

E-commerce users' preferences for delivery options

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.

econ.GN↗

arXiv2022

A case study of the profit-maximizing multi-vehicle pickup and delivery selection problem for the road networks with the integratable nodes

This paper is a study of an application-based model in profit-maximizing multi-vehicle pickup and delivery selection problem (PPDSP). The graph-theoretic model proposed by existing studies of PPDSP is based on transport requests to define the corresponding nodes (i.e., each request corresponds to a pickup node and a delivery node). In practice, however, there are probably multiple requests coming from or going to an identical location. Considering the road networks with the integratable nodes as above, we define a new model based on the integrated nodes for the corresponding PPDSP and propose a novel mixed-integer formulation. In comparative experiments with the existing formulation, as the number of integratable nodes increases, our method has a clear advantage in terms of the number of variables as well as the number of constraints required in the generated instances, and the accuracy of the optimized solution obtained within a given time.

cs.DM↗

arXiv2022

Towards understanding network topology and robustness of logistics systems

Advanced integration of logistics systems has been promoted for the sake of competitiveness and sustainability. Such efforts will enable more globally optimal and flexible operations by efficiently utilizing transportation capacity. At the same time, interconnection of transport operations increases complexity at a network level, which reduces the predictability of the response of the system to disruptions. However, our understanding of the behavior of such systems is still limited. In particular, the topology of the network, which changes as the systems are integrated, is an important factor that affects the performance of the entire system. Knowledge of such mechanisms would be useful in the design and evaluation of integrated logistics. Here, we developed a simple mathematical model that extracts the essence of the problem and performed extensive numerical experiments by Monte Carlo simulations for three scenarios that mimic changes in demand: (i) locally and temporally increased traffic demand, (ii) globally and temporally increased traffic demand, and (iii) permanent change in demand pattern, under various conditions on the type of route-finding algorithm, network structure, and transportation capacity. Adaptive route-finding algorithms were more effective in square lattice and random networks, which contained many bypass routes, than in hub-and-spoke networks. Furthermore, the square lattice and random networks were robust to the change in the demand pattern and temporal blockage of delivery paths (e.g., due to high demand). We suggest that such preferable properties are only present in networks with redundancy and that the bypass structure is an important criterion for designing network logistics.

physics.soc-ph↗

arXiv2021

Model retraining and information sharing in a supply chain with long-term fluctuating demands

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.

physics.soc-ph↗