Max Klimm

Max Klimm, Marc E. Pfetsch, Martin Skutella et al.

We develop efficient algorithms for a fundamental network design problem arising in potential-based flow models, which are central to many energy transport networks (e.g., hydrogen and electricity). In contrast to classical network flow problems, the nonlinearities inherent in potential-based networks introduce significant new challenges. We address these challenges through intricate reductions to classical combinatorial optimization problems, such as (constrained) shortest path problems, enabling the application of well-established algorithmic techniques to compute exact and approximate solutions efficiently. Finally, we complement these algorithmic results with matching complexity results concerning the hardness and non-approximability of the considered problem variants.

Javier Cembrano, Felix Fischer, Max Klimm

In the symmetric rendezvous problem two players follow the same (randomized) strategy to visit one of $n$ locations in each time step $t=0,1,2,\dots$. Their goal is to minimize the expected time until they visit the same location and thus meet. Anderson and Weber [J. Appl. Prob., 1990] proposed a strategy that operates in rounds of $n-1$ steps: a player either remains in one location for $n-1$ steps or visits the other $n-1$ locations in random order; the choice between these two options is made with a probability that depends only on $n$. The strategy is known to be optimal for $n=2$ and $n=3$, and there is convincing evidence that it is not optimal for $n=4$. We show that it is not optimal for any $n\geq 4$, by constructing a strategy with a smaller expected meeting time.

Max Klimm, Martin Knaack

We study an online item-allocation problem with budgets and a submodular objective. A set of $m$ agents is known in advance, and each agent $j$ has a known budget. A set of $n$ items arrives over time in a uniformly random order. When item $i$ arrives, its cost $c_{i,j}$ for each agent $j$ is revealed, and the algorithm must irrevocably assign $i$ to an agent without violating any budget constraint. The goal is to maximize a monotone submodular function defined over all possible assignments $[n] \times [m]$. At the time of decision, the algorithm has only oracle access to this submodular function restricted to items seen so far. This model subsumes welfare maximization with submodular valuations, agent-specific item costs, and agent-specific budgets. We measure the performance of an algorithm by its competitive ratio, i.e., the worst-case ratio between the algorithm's expected value and that of the offline optimum, which knows all item costs and the full submodular function in advance. Prior work only considered the case of a single agent and achieved a $1/54.4$-competitive algorithm. We generalize and improve this result to a polynomial-time $α$-competitive algorithm with $α\approx 1/14.85$ for an arbitrary number of agents. We also study the special case in which all item costs and all budgets equal $1$, which yields an online submodular matching problem. Prior work achieved a polynomial $1/9.66$-competitive algorithm for this problem; we improve this to a factor of $1/6.86$. Both our algorithms rely on repeatedly computing $(1 - 1/e)$-approximations of the multilinear extensions of offline variants of the subproblems. If super-polynomial runtime is allowed, these subproblems can be solved optimally, and our competitive ratios improve by this factor.

Javier Cembrano, Felix Fischer, Max Klimm

We study the selection of agents based on mutual nominations, a theoretical problem with many applications from committee selection to AI alignment. As agents both select and are selected, they may be incentivized to misrepresent their true opinion about the eligibility of others to influence their own chances of selection. Impartial mechanisms circumvent this issue by guaranteeing that the selection of an agent is independent of the nominations cast by that agent. Previous research has established strong bounds on the performance of impartial mechanisms, measured by their ability to approximate the number of nominations for the most highly nominated agents. We study to what extent the performance of impartial mechanisms can be improved if they are given a prediction of a set of agents receiving a maximum number of nominations. Specifically, we provide bounds on the consistency and robustness of such mechanisms, where consistency measures the performance of the mechanisms when the prediction is accurate and robustness its performance when the prediction is inaccurate. For the general setting where up to $k$ agents are to be selected and agents nominate any number of other agents, we give a mechanism with consistency $1-O\big(\frac{1}{k}\big)$ and robustness $1-\frac{1}{e}-O\big(\frac{1}{k}\big)$. For the special case of selecting a single agent based on a single nomination per agent, we prove that $1$-consistency can be achieved while guaranteeing $\frac{1}{2}$-robustness. A close comparison with previous results shows that (asymptotically) optimal consistency can be achieved with little to no sacrifice in terms of robustness.

Tobias Harks, Mona Henle, Max Klimm et al.

We study a multi-leader single-follower congestion game where multiple users (leaders) choose one resource out of a set of resources and, after observing the realized loads, an adversary (single-follower) attacks the resources with maximum loads, causing additional costs for the leaders. For the resulting strategic game among the leaders, we show that pure Nash equilibria may fail to exist and therefore, we consider approximate equilibria instead. As our first main result, we show that the existence of a $K$-approximate equilibrium can always be guaranteed, where $K \approx 1.1974$ is the unique solution of a cubic polynomial equation. To this end, we give a polynomial time combinatorial algorithm which computes a $K$-approximate equilibrium. The factor $K$ is tight, meaning that there is an instance that does not admit an $α$-approximate equilibrium for any $α<K$. Thus $α=K$ is the smallest possible value of $α$ such that the existence of an $α$-approximate equilibrium can be guaranteed for any instance of the considered game. Secondly, we focus on approximate equilibria of a given fixed instance. We show how to compute efficiently a best approximate equilibrium, that is, with smallest possible $α$ among all $α$-approximate equilibria of the given instance.