SearcharxivSearch

arXiv · 2605.07773

Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents

Abstract

The short-board effect, analogous to Liebig's Law of the Minimum, postulates that the collective performance of a team is constrained by its weakest component. This principle has profound implications for the optimization of collaboration in a variety of contexts, including management, education, and organizational structures. Despite its theoretical significance, empirical validation remains elusive due to challenges of assessing individual capabilities, controlling real-world variables, and data biases towards successful outcomes, as well as high employee turnover.To address this absence of knowledge, we employ multi-agents driven by large language models to simulate a teamwork with standard operating procedure, revealing the relationship between individual capability and collective team performance.In homogeneous team configurations, three capability regimes are observed, particularly the Sisyphus predicament state at the critical capability threshold characterized by extensive ineffective efforts and pseudo-high efficiency. Furthermore, with a single weak link quantifying the short-board effect, we highlight different impacts across core and non-core members on the team performance.More importantly, when the team exhibits multiple weak links, a cumulative product effect emerges, demonstrating that team performance is shaped by the aggregated impact of all weaknesses rather than the weakest link solely.This suggests that mitigation strategies should extend beyond the remediation of individual weak links.These findings rigorously elaborate the short-board theory and provide actionable insights to optimize team management, organizational operations, and supply chain resilience.

Explore related subjects

Keep this discovery

BibTeXRIS

Xin Xu, Jiu Zhang, Xiao-Ling Lei, Xiong-Fei Jiang, Long Xiong. 2026-05-08. Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents. https://arxiv.org/abs/2605.07773

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

KEEP EXPLORING

Related papers

Energy pathway variety and the progress of the energy transition in European countries

The integration of new energy forms into existing energy infrastructure has emerged as a critical challenge in the context of the pursuit of a sustainable energy transition. One of the main challenges is understanding how this integration takes place not only from the introduction, but also as energy follows existing paths or creates new ones through which it is transformed and used by different activities. Here we introduce techniques from network science to analyse this process for the case of 29 European countries between 1992 and 2021. We study how new energy forms increase or decrease the variety (heterogeneity) of paths through the system of each country by establishing new ones and replacing or phasing out existing ones. We find that the transition to systems based on renewable energy is characterised by an initial increase in the variety of paths while the heterogeneity of paths decreases at the end of the transition, when the proportion of non-renewables in the system tends to zero. We then demonstrate that greater heterogeneity (complexity) is associated with larger annual fluctuations in the proportion of non-renewable sources in the system, establishing a direct relationship between the progress of the transition and the complexity of the energy system in which it occurs. This contributes to the understanding of general properties of the dynamics of the energy transition and effects that accelerate or deter it.

physics.soc-ph

Fundamental limits to identifying node and tie memory in temporal networks: marginal artefacts and spreading dynamics

Temporal-network models attribute memory in contact data to either node self-excitation (branching ratio n_node) or tie reinforcement (kappa), carrying major consequences for epidemic spreading. We prove that when event initiators are observed, the two mechanisms are orthogonal: the Fisher information is block-diagonal and neither trades off against the other. In undirected proximity data, where initiators are unobserved, marginalising over them couples the mechanisms into a structural confound that survives posterior smoothing. On empirical proximity, messaging, and email records, however, a cruder failure dominates: fitted node memory is pinned to the inter-event marginal law and remains virtually invariant across latent label posterior samples (coefficient of variation below 1%). An inter-event-order shuffle test and burstiness-memory diagnostics reveal that exponential-Hawkes node memory is recovered from none, while tie reinforcement remains identifiable throughout. This near-unidentifiability is intrinsic, not an artefact of the exponential kernel: refitting flexible scale-free (sum-of-exponentials) kernels on synthetic power-law self-exciting processes fails to distinguish genuine node memory from memoryless renewal controls, with identical collapses recurring on algorithmic networks (edit bots, cloud microservices) and cortical spiking. Downstream epidemic consequences are quantitative: simulations fitted to empirical contact records under-predict outbreak sizes by up to a factor of 2.5 and shift the epidemic threshold. We conclude that observational temporal networks face a two-fold identifiability boundary: contact directionality is essential to decouple tie reinforcement, whereas heavy-tailed node self-excitation is intrinsically unidentifiable from contact timings alone.

physics.soc-ph

Assessing extreme flood impacts on urban rail transit: A passenger-oriented, resilience-informed framework

Urban rail transit systems (URTSs) are increasingly exposed to extreme floods following heavy precipitation, yet passenger travel impacts are often assessed through delay-based indicators that overlook infeasible journeys under large-scale disruptions. This study develops a passenger-oriented, resilience-informed framework for assessing flood impacts on URTS journeys from disruption onset to recovery completion. The framework presents a novel six-category classification of journey impacts, explicitly considering rerouting, alternative station use, and a delay threshold. It is demonstrated through hourly dynamic simulations of 15 London URTS lines under 30-year, 100-year, and 1,000-year flood risk scenarios. Results indicate that severe flood disruptions lead to substantial unsatisfied demand, driven primarily by unavailable routes rather than unacceptable delays. Compared with finer behaviour adjustments, rerouting dominates travel impacts. These findings highlight the significance of moving beyond delay-based assessment and provide valuable evidence on essential behavioural mechanisms for strategic-level stress testing intended to inform URTS flood resilience intervention planning.

physics.soc-ph