SearcharxivSearch

arXiv · 2607.02765

Epistemic Horizon Minority Games: When Abundance Reduces Strategic Value

Abstract

Strategic value can fall when an option becomes visible. A route, signal, bet, or opportunity may be attractive because few agents see it; public attention can erase the advantage it reveals. We formalize this mechanism as an epistemic-horizon minority game (EHMG), where agents have bounded observation horizons, action-specific awareness, desire-biased utilities, and payoffs that decline with crowding, and where the object is not a fixed congestion game with omitted actions but an awareness-transition game on a finite lattice. We prove fixed-awareness potential-game reduction, finite monotone awareness convergence, logit mean-field uniqueness under an explicit norm condition, non-reducibility from static count-based congestion games, and sensitivity bounds for nonlinear revelation. We separate the target price of information from aggregate welfare loss, showing that they can coincide, diverge, or recommend opposite disclosure policies, while modeling private revelation, public common revelation, and correlated group disclosure as distinct signal structures with different equilibrium effects. Experiments regenerate awareness sweeps, public visibility shocks, horizon-desire grids, information-constrained Braess examples, disclosure optimization, minimum harmful revelation, and counterfactual baselines isolating the epistemic mechanism from ordinary full-awareness congestion. Strategic trace encodings are evaluated as a controlled regime-recognition benchmark using raw trajectories, Fourier summaries, recurrence and Gramian images, image bundles, local-filter features, leakage probes, phase-scrambled controls, resolution and recurrence-threshold sweeps, spectral carriers, and IAAFT-matched null parameter-shift controls to test whether trace encodings recover strategic structure under robust nulls.

Explore related subjects

Keep this discovery

BibTeXRIS

Faruk Alpay, Levent Sarioglu. 2026-07-02. Epistemic Horizon Minority Games: When Abundance Reduces Strategic Value. https://arxiv.org/abs/2607.02765

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

MMS Allocation for Chores with Online Agent Arrivals

We study the fair allocation of $m$ indivisible chores to $n$ agents with subadditive cost functions arriving online in an arbitrary order. Upon an agent's arrival, we are informed of her cost function and must irrevocably assign her a set of chores. We focus on the Maximin Share (MMS) fairness notion and aim to compute an allocation in which all items are assigned, and no agent incurs a cost more than $\alpha$ times her MMS. Without any prior information about the instance (other than $n$ and $m$), we design an algorithm with a competitive ratio of $O(\min\{n, k\log^{1+\epsilon}k, \log m\})$ for any constant $\epsilon > 0$, where $k$ denotes the number of cost function types. Our bound matches the best known offline approximation guarantees for MMS under subadditive costs and is nearly optimal with respect to all three parameters: we show that even for binary additive cost functions, no online algorithm can achieve a competitive ratio of $o(\min\{n, k\log k, \log m\})$. We then consider the setting in which the $k$ cost function types are known in advance (though the realized types of arriving agents are not). For additive cost functions, we provide an algorithm with a competitive ratio of $O(\min\{\log k, \log(kn)/\log\log(kn)\})$, and show that constant-competitive algorithms do not exist for general $k$, even for the binary additive setting. For binary additive functions when $k \le n$, we propose a $3$-competitive algorithm and establish a lower bound of $2$.

cs.GT

Truncated Noisy Best-Response Algorithms: Toward Game Theoretic Learning with Safety Guarantees

We consider a game theoretic approach to solve multi-agent coordination problems with submodular maximization objectives. It is known for such problems that the Nash equilibria for the corresponding game are always within 50% of the optimal, but that the equilibria which achieve this worst-case bound are not stable. To exploit this instability, we propose a family of algorithms which we call Truncated Noisy Best-Response (TNBR) Algorithms. These algorithms are flexibly characterized by agents asynchronously and stochastically selecting actions from a neighbourhood of their best response payoffs. We compute bounds on the recurrent classes of TNBR algorithms' associated Markov chains. Our bounds fall into two categories: first, "Performance" bounds ensure that TNBR algorithms always have a high-value recurrent state; second, "Safety" bounds ensure that TNBR algorithms never have arbitrarily-bad recurrent states. Furthermore, these two types of bounds are linked by a waterbed-like effect: every game with a poor Safety guarantee necessarily has a favorable Performance guarantee.

cs.GT

Existence of the Core in Approval-Based Committee Elections

We settle the main open question in the theory of approval-based multi-winner elections: we show that there always exists a committee in the core. The core is a stability and group fairness concept. The proof introduces a new voting rule that optimizes an entropy-like objective function over committees and payment systems. All local optima of this objective function lie in the core, which implies that a core committee can be found in polynomial time.

cs.GT