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Abstract Design researchers have struggled to produce quantitative predictions for exactly why and when diversity might help or hinder design search efforts. This paper addresses that problem by studying one ubiquitously used search strategy—Bayesian optimization (BO)—on a 2D test problem with modifiable convexity and difficulty. Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-determinantal point process method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples. We initially found, to our surprise, that diversity did not appear to affect BO, neither helping nor hurting the optimizer’s convergence. However, follow-on experiments illuminated a key trade-off. Non-diverse initial samples hastened posterior convergence for the underlying model hyper-parameters—a model building advantage. In contrast, diverse initial samples accelerated exploring the function itself—a space exploration advantage. Both advantages help BO, but in different ways, and the initial sample diversity directly modulates how BO trades those advantages. Indeed, we show that fixing the BO hyper-parameters removes the model building advantage, causing diverse initial samples to always outperform models trained with non-diverse samples. These findings shed light on why, at least for BO-type optimizers, the use of diversity has mixed effects and cautions against the ubiquitous use of space-filling initializations in BO. To the extent that humans use explore-exploit search strategies similar to BO, our results provide a testable conjecture for why and when diversity may affect human-subject or design team experiments. 

Sharing, reuse, and synthesis of knowledge is central to the research process. These core functions are in theory served by the system of monographs, abstracts, and papers in journals and proceedings, with citation indices and search databases that comprise the core of our formal scholarly communication infrastructure; yet, converging lines of empirical and anecdotal evidence suggest that this system does not adequately act as infrastructure for synthesis. Emerging developments in new institutions for science, along with new technical infrastructures and tooling for decentralized knowledge work, offer new opportunities to prototype new technical infrastructures on top of a different installed base than the publish or perish, neoliberal academy. This workshop aims to integrate these developments and communities with CSCW’s deep roots in knowledge infrastructures and collaborative and distributed sensemaking, with new developments in science institutions and tooling, to stimulate and accelerate progress towards prototyping new scholarly communication infrastructures that are actually optimized for sharing, reusing, and synthesizing knowledge. 

Analogy—the ability to find and apply deep structural patterns across domains—has been fundamental to human innovation in science and technology. Today there is a growing opportunity to accelerate innovation by moving analogy out of a single person’s mind and distributing it across many information processors, both human and machine. Doing so has the potential to overcome cognitive fixation, scale to large idea repositories, and support complex problems with multiple constraints. Here we lay out a perspective on the future of scalable analogical innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that quantitatively demonstrate the promise of the approach, as well as core challenges critical to realizing this vision.