Business Strategy and Innovation

Últimos artículos

• Coopetition-Gym v1: A Formally Grounded Platform for Mixed-Motive Multi-Agent Reinforcement Learning under Strategic Coopetition

  Vik Pant, Eric Yu · 5 de mayo de 2026

  We present Coopetition-Gym v1, a benchmark platform for mixed-motive multi-agent reinforcement learning under strategic coopetition. The platform comprises twenty environments organized into four mechanism classes that correspond to four foundational technical reports: interdependence and complement…

• ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models

  Rui Xu, Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Shiqing Xin, Changhe Tu, Taku Komura, Wenping Wang · 30 de abril de 2026

  In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and for various structured generation tasks, additional attributes are combined to associate with data samples. We show that…

• Computational Foundations for Strategic Coopetition: Formalizing Sequential Interaction and Reciprocity

  Vik Pant, Eric Yu · 3 de abril de 2026

  Strategic coopetition in multi-stakeholder systems requires understanding how cooperation persists through time without binding contracts. This technical report extends computational foundations for strategic coopetition to sequential interaction dynamics, bridging conceptual modeling (i* framework)…

• When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values

  Christophe Muller (LPSM), Erwan Scornet (LPSM), Julie Josse (PREMEDICAL) · 3 de febrero de 2026

  Predicting with missing inputs challenges even parametric models, as parameter estimation alone is insufficient for prediction on incomplete data. While several works study prediction in linear models, we focus on logistic models, where optimal predictors lack closed-form expressions. We prove that …

• Computational Foundations for Strategic Coopetition: Formalizing Collective Action and Loyalty

  Vik Pant, Eric Yu · 26 de enero de 2026

  Mixed-motive multi-agent settings are rife with persistent free-riding because individual effort benefits all members equally, yet each member bears the full cost of their own contribution. Classical work by Holmstr\"om established that under pure self-interest, Nash equilibrium is universal shirkin…

• Computational Foundations for Strategic Coopetition: Formalizing Trust and Reputation Dynamics

  Vik Pant, Eric Yu · 22 de enero de 2026

  Modern socio-technical systems increasingly involve multi-stakeholder environments where actors simultaneously cooperate and compete. These coopetitive relationships exhibit dynamic trust evolution based on observed behavior over repeated interactions. While conceptual modeling languages like i* rep…

• Trading off Consistency and Dimensionality of Convex Surrogates for the Mode

  Enrique Nueve, Bo Waggoner, Dhamma Kimpara, Jessie Finocchiaro · 21 de enero de 2026

  In multiclass classification over $n$ outcomes, the outcomes must be embedded into the reals with dimension at least $n-1$ in order to design a consistent surrogate loss that leads to the "correct" classification, regardless of the data distribution. For large $n$, such as in information retrieval a…

• Computational Foundations for Strategic Coopetition: Formalizing Interdependence and Complementarity

  Vik Pant, Eric Yu · 1 de diciembre de 2025

  Modern socio-technical systems are characterized by strategic coopetition where actors simultaneously cooperate to create value and compete to capture it. While conceptual modeling languages like i* provide rich qualitative representations of strategic dependencies, they lack mechanisms for quantita…

• A Small Math Model: Recasting Strategy Choice Theory in an LLM-Inspired Architecture

  Roussel Rahman, Jeff Shrager · 24 de noviembre de 2025

  Strategy Choice Theory (SCT; Siegler and Shrager, 1984; Siegler, 2000) explains important aspects of children's arithmetic learning based upon principles including learning from developmentally naturalistic data, probabilistic representation, confidence-based retrieval, and the phase-like importance…