Paper by Associate Professor Yasushi Kawase of Value Exchange Engineering Accepted at AAAI-26

A paper by associate professor Yasushi Kawase, affiliated with Value Exchange Engineering, a joint research program between Mercari R4D and the University of Tokyo’s Inclusive Engineering Collaborative Research Organization (RIISE), was accepted at the 40th AAAI Conference on Artificial Intelligence (AAAI-26)—a top conference in the field of artificial intelligence—held in Singapore from January 20 to 27, 2026.

Paper Details

• Title

  • Sequential Selling with Sunk Cost Bias

• Writers:

  • Yasushi Kawase (Tokyo University, associate professor of Value Exchange Engineering)
  • Tomohiro Nakayoshi (Tokyo University Graduate School of Information Science and Technology, undertaking a master’s program at the time)

• Overview

  • This study theoretically analyzes the influence of sunk cost bias (sensitivity λ), which is dragged by the past accumulated cost c(t−1), in a sequential selling problem (optimal stopping problem) where a daily holding cost c occurs. First, we defined the agent’s subjective utility when selling at price Xt on day t as Xt−λc(t−1), and mathematically formulated the strength of the bias. Then, based on the predictability of the evolution of their own bias, we classified agents into three types: “Optimistic” (I care about sunk costs today, but my future self tomorrow should be able to decide rationally), “Naive” (I will probably decide the same way as I do now from now on), and “Sophisticated” (My future self tomorrow will probably be dragged by sunk costs as well). As an analytical approach, we calculated the “difference in objective profit in the worst-case scenario (loss)” compared to an agent without bias. Unlike conventional models that deal with reaching rewards on a graph structure, we adopted a sequential selling framework where rewards change dynamically. The novelty lies in quantifying the gap in expected objective profit compared to a rational agent as a function of the time axis T. Through the analysis, we derived the following results:

    1. Optimistic type: They misidentify that their future self is rational, and linearly raise the selling threshold to recover past costs. The loss in the worst-case scenario is λc(T−1)(T−2)/2, which worsens quadratically with respect to time.
    2. Naive type: Because the bias is canceled out in the decision-making process, they follow the same policy as rational individuals, and the difference in expected profit becomes 0.
    3. Sophisticated type: By predicting future bias accumulation and trying to sell early, they suppress the loss to a linear function of λc(T−1).

    We succeeded in introducing sunk cost bias into the sequential selling problem and mathematically identifying the profit loss for each type.

• Details

  • UTokyo Repository：https://repository.dl.itc.u-tokyo.ac.jp/records/2014288
  • UTokyo Self-archive：https://r.dl.itc.u-tokyo.ac.jp/esploro/outputs/9913209609301

Overview of AAAI-26

• Date and time
  • January 20–27, 2026
• Location
  • Singapore EXPO
• Event page
  • https://aaai.org/conference/aaai/aaai-26/

Mercari R4D webpage on the Value Exchange Engineering project: https://r4d.mercari.com/en/vxe/