Strategically Revealing Intentions in General Lotto Games

Strategic decision-making in uncertain and adversarial environments is crucial for the security of modern systems and infrastructures. A salient feature of many optimal decision-making policies is a level of unpredictability, or randomness, which helps to keep an adversary uncertain about the system’s behavior. This paper seeks to explore decision-making policies on the other end of the spectrum – namely, whether there are benefits in revealing one’s strategic intentions to an opponent before engaging in competition.We study these scenarios in a well-studied model of competitive resource allocation problem known as General Lotto games. In the classic formulation, two competing players simultaneously allocate their assets to a set of battlefields, and the resulting payoffs are derived in a zero-sum fashion. Here, we consider a multi-step extension where one of the players has the option to publicly pre-commit assets in a binding fashion to battlefields before play begins. In response, the opponent decides which of these battlefields to secure (or abandon) by matching the pre-commitment with its own assets. They then engage in a General Lotto game over the remaining set of battlefields. Interestingly, this paper highlights many scenarios where strategically revealing intentions can actually significantly improve one’s payoff. This runs contrary to the conventional wisdom that randomness should be a central component of decision-making in adversarial environments

Strategically Revealing Intentions in General Lotto Games

Strategic decision-making in uncertain and adversarial environments is crucial for the security of modern systems and infrastructures. A salient feature of many optimal decision-making policies is a level of unpredictability, or randomness, which helps to keep an adversary uncertain about the system’s behavior. This paper seeks to explore decision-making policies on the other end of the spectrum – namely, whether there are benefits in revealing one’s strategic intentions to an opponent before engaging in competition.We study these scenarios in a well-studied model of competitive resource allocation problem known as General Lotto games. In the classic formulation, two competing players simultaneously allocate their assets to a set of battlefields, and the resulting payoffs are derived in a zero-sum fashion. Here, we consider a multi-step extension where one of the players has the option to publicly pre-commit assets in a binding fashion to battlefields before play begins. In response, the opponent decides which of these battlefields to secure (or abandon) by matching the pre-commitment with its own assets. They then engage in a General Lotto game over the remaining set of battlefields. Interestingly, this paper highlights many scenarios where strategically revealing intentions can actually significantly improve one’s payoff. This runs contrary to the conventional wisdom that randomness should be a central component of decision-making in adversarial environments

The Non-Constant-Sum Colonel Blotto Game

The Colonel Blotto game is a two-player constant-sum game in which each player simultaneously distributes his fixed level of resources across a set of contests. In the traditional formulation of the Colonel Blotto game, the players’ resources are “use it or lose it” in the sense that any resources which are not allocated to one of the contests are forfeited. This article examines a non-constant-sum version of the Colonel Blotto game which relaxes this use it or lose it feature. We find that if the level of asymmetry between the players’ budgets is below a threshold, then there exists a oneto- one mapping from the unique set of equilibrium univariate marginal distribution functions in the constant-sum game to those in the non-constant-sum game. Once the asymmetry of the players’ budgets exceeds the threshold this relationship breaks down and we construct a new equilibrium.Colonel Blotto Game, All-Pay Auction, Contests, Mixed Strategies

Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks

We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. Such game access is common in reinforcement learning settings, where the environment is typically treated as a black box. To tackle this problem, we apply zeroth-order optimization techniques that combine smoothed gradient estimators with equilibrium-finding dynamics. We model players' strategies using artificial neural networks. In particular, we use randomized policy networks to model mixed strategies. These take noise in addition to an observation as input and can flexibly represent arbitrary observation-dependent, continuous-action distributions. Being able to model such mixed strategies is crucial for tackling continuous-action games that lack pure-strategy equilibria. We evaluate the performance of our method using an approximation of the Nash convergence metric from game theory, which measures how much players can benefit from unilaterally changing their strategy. We apply our method to continuous Colonel Blotto games, single-item and multi-item auctions, and a visibility game. The experiments show that our method can quickly find high-quality approximate equilibria. Furthermore, they show that the dimensionality of the input noise is crucial for performance. To our knowledge, this paper is the first to solve general continuous-action games with unrestricted mixed strategies and without any gradient information

The Robustness of ‘Enemy-of-My-Enemy-is-My-Friend’ Alliances

This paper examines the robustness of alliance formation in a three-player, two-stage game in which each of two players compete against a third player in disjoint sets of contests. Although the players with the common opponent share no common interests, we find that under the lottery contest success function (CSF) there exists a range of parameter configurations in which the players with the common opponent have incentive to form an alliance involving a pre-conflict transfer of resources. Models that utilize the lottery CSF typically yield qualitatively different results from those arising in models with the auction CSF (Fang 2002). However, under the lottery and the auction CSFs, the parameter configurations within which players with a common opponent form an alliance are closely related. Our results, thus, provide a partial robustness result for ‘enemy-of-my-enemy-is-my-friend’ alliances.Alliance Formation, Contests, Economics of Alliances, Conflict

Coalitional Colonel Blotto Games with Application to the Economics of Alliances

In Borel’s (1921) Colonel Blotto game two players simultaneously allocate their respective endowments of a resource across n battlefields, the higher allocation wins each battlefield, and players maximize the number of battlefields won. Here we examine two players who may form an alliance before separately competing in two disjoint Colonel Blotto games against a common adversary. Despite a lack of common interests, unilateral transfers — in a direction consistent with the exploitation hypothesis — arise for a range of parameter configurations. Such transfers alter the adversary’s strategy and the combination of the direct and strategic effects benefits both allies

The Optimal Defense of Networks of Targets

This paper examines a game-theoretic model of attack and defense of multiple networks of targets in which there exist intra-network strategic complementarities among targets. The defender’s objective is to successfully defend all of the networks and the attacker’s objective is to successfully attack at least one network of targets. Although there are multiple equilibria, we characterize correlation structures in the allocations of forces across targets that arise in all equilibria. For example, in all equilibria the attacker utilizes a stochastic ‘guerrilla warfare’ strategy in which a single random network is attacked

Approximate Equilibria in Non-constant-sum Colonel Blotto and Lottery Blotto Games with Large Numbers of Battlefields

In the Colonel Blotto game, two players with a fixed budget simultaneously allocate their resources across n battlefields to maximize the aggregate value gained from the battlefields where they have the higher allocation. Despite its long-standing history and important applicability, the Colonel Blotto game still lacks a complete Nash equilibrium characterization in its most general form-the non-constant-sum version with asymmetric players and heterogeneous battlefields. In this work, we propose a simply-constructed class of strategies-the independently uniform strategies-and we prove them to be approximate equilibria of the non-constant-sum Colonel Blotto game; moreover, we also characterize the approximation error according to the game's parameters. We also introduce an extension called the Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts. We prove that the proposed strategies are also approximate equilibria of the Lottery Blotto game