Online Coalition Formation under Random Arrival or Coalition Dissolution

Coalition formation considers the question of how to partition a set of $n$ agents into disjoint coalitions according to their preferences. We consider a cardinal utility model with additively separable aggregation of preferences and study the online variant of coalition formation, where the agents arrive in sequence and whenever an agent arrives, they have to be assigned to a coalition immediately. The goal is to maximize social welfare. In a purely deterministic model, the greedy algorithm, where an agent is assigned to the coalition with the largest gain, is known to achieve an optimal competitive ratio, which heavily relies on the range of utilities. We complement this result by considering two related models. First, we study a model where agents arrive in a random order. We find that the competitive ratio of the greedy algorithm is $\Theta\left(\frac{1}{n^2}\right)$, whereas an alternative algorithm, which is based on alternating between waiting and greedy phases, can achieve a competitive ratio of $\Theta\left(\frac{1}{n}\right)$. Second, we relax the irrevocability of decisions by allowing to dissolve coalitions into singleton coalitions, presenting a matching-based algorithm that once again achieves a competitive ratio of $\Theta\left(\frac{1}{n}\right)$. Hence, compared to the base model, we present two ways to achieve a competitive ratio that precisely gets rid of utility dependencies. Our results also give novel insights in weighted online matching.Comment: Appears in the 31st Annual European Symposium on Algorithms (ESA 2023

Topological Distance Games

We introduce a class of strategic games in which agents are assigned to nodes of a topology graph and the utility of an agent depends on both the agent's inherent utilities for other agents as well as her distance from these agents on the topology graph. This model of topological distance games (TDGs) offers an appealing combination of important aspects of several prominent settings in coalition formation, including (additively separable) hedonic games, social distance games, and Schelling games. We study the existence and complexity of stable outcomes in TDGs -- for instance, while a jump stable assignment may not exist in general, we show that the existence is guaranteed in several special cases. We also investigate the dynamics induced by performing beneficial jumps.Comment: Appears in the 37th AAAI Conference on Artificial Intelligence (AAAI), 202

Superiority of Instantaneous Decisions in Thin Dynamic Matching Markets

We study a dynamic matching procedure where homogeneous agents arrive at random according to a Poisson process and form edges at random yielding a sparse market. Agents leave according to a certain departure distribution and may leave early by forming a pair with a compatible agent. The primary objective is to maximize the number of matched agents. Our main result is to show that a mild condition on the departure distribution suffices to get almost optimal performance of instantaneous matching, despite operating in a thin market. We are thus the first to provide a natural condition under which instantaneous decisions are superior in a market that is both sparse and thin. This result is surprising because similar results in the previous literature are based on market thickness. In addition, instantaneous matching performs well with respect to further objectives such as minimizing waiting times and avoiding the risk of market congestion. We develop new techniques for proving our results going beyond commonly adopted methods for Markov processes.Comment: Appears in the 24th ACM Conference on Economics and Computation (EC), 202

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Single nucleotide polymorphism (SNP) microarray data. SNP data underlying the finding in this article. (Rdata 50688 kb

Welfare Guarantees in Schelling Segregation

Schelling’s model is an influential model that reveals how individual perceptions and incentives can lead to residential segregation. Inspired by a recent stream of work, we study welfare guarantees and complexity in this model with respect to several welfare measures. First, we show that while maximizing the social welfare is NP-hard, computing an assignment of agents to the nodes of any topology graph with approximately half of the maximum welfare can be done in polynomial time. We then consider Pareto optimality, introduce two new optimality notions based on it, and establish mostly tight bounds on the worst-case welfare loss for assignments satisfying these notions as well as the complexity of computing such assignments. In addition, we show that for tree topologies, it is possible to decide whether there exists an assignment that gives every agent a positive utility in polynomial time; moreover, when every node in the topology has degree at least 2, such an assignment always exists and can be found efficiently

Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington’s disease

State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications

Cross-cultural adaptation into Punjabi of the English version of the Hospital Anxiety and Depression Scale

BACKGROUND: We wanted to use a Punjabi version of the Hospital Anxiety and Depression Scale (HADS) to enable non-English speaking patients to participate in a clinical trial. The aim of the study was to translate and validate the Hospital Anxiety and Depression Scale into Punjabi. METHODS: The HADS was translated into Punjabi by a multidisciplinary team, verified against the original version, and administered to 73 bilingual patients attending an outpatient clinic. RESULTS: One sample t-tests and the Bland-Altman plots demonstrated acceptable linguistic agreement between the two versions of the HADS. Spearman's rank-order correlation coefficients (p < 0.0001) demonstrate excellent conceptual agreement between each item and its corresponding subscale score, for both versions. Concordance rates revealed that the Punjabi HADS adequately identified borderline cases of anxiety (80.8%), definite cases of anxiety (91.8%) and depression (91.8%), but was less reliable in identifying borderline cases of depression (65.8%). Cronbach alpha coefficients revealed high levels of internal consistency for both the Punjabi and English versions (0.81 and 0.86 for anxiety and 0.71 and 0.85 for depression, respectively). CONCLUSION: The Punjabi HADS is an acceptable, reliable and valid measure of anxiety and depression among physically ill Punjabi speaking people in the United Kingdom