Zara Anwarzai

The influence of network structure on collective problem-solving is a central focus in collective intelligence. However, the causal mechanisms linking structure to collective outcomes remain unexplored. To explore these, we utilize an agent-based model, the Potions Task, which operationalizes problem-solving as a combinatorial process suited to information-theoretic analysis. We examine information-based metrics at the level of agent pairs across networks, analyzing variation over time to determine how they predict problem-solving across network structures. We measure redundancy (or conversely, synergy) of solutions discovered by agents with respect to the net-work’s global knowledge. Building on findings that small-world networks support efficient problem-solving, our results uncover the underlying mechanism by which high-performing networks achieve efficiency, namely by balancing local redundancy with long-range synergy. Furthermore, we find that synergy consistently predicts group performance, including in network structures typically considered inefficient at complex tasks. Synergy in information processing, measured at both local and global levels, therefore mediates the effects of structure and can override them entirely, suggesting that information-processing synergy, rather than network structure itself, is the primary driver of group performance. By formalizing a causal framework for information processing in collectives, our study provides a mechanistic account of collective problem-solving beyond network structure alone.

Collective intelligence research spans multiple disciplines and focuses on a broad range of collective behaviors, including group problem-solving, flocking in social animals, and the formation of social knowledge. It is not apparent what these different forms of collective intelligence have in common, apart from being instances of collective behavior. In this paper, we develop a framework that enables us to better classify different forms of collectively intelligent behavior in relation to one another based on the information processing mechanisms involved. We argue that these behaviors share a common foundation, which we call collective information processing, or CIP. CIP involves two key mechanisms: (1) individual processing of group information and (2) group processing, or group-level sensitivity to the arrangement of individual information. We operationalize the CIP framework to analyze different forms of collective intelligence, both classifying them in relation to one another and in alignment with generalized quantifiable measures of information processing. Our account of collective intelligence as CIP offers a novel framework for identifying and classifying forms of collective intelligence across a wide range of disciplinary contexts. This framework is meant to unify and subsume, rather than simply challenge, existing attempts to define collective intelligence.

Graduate employees are an untapped source of union power in the South. They are an exceptionally pro-union and militant group of workers with the potential to expand labor’s footprint in a part of the country essential for raising standards for all workers. Their joint student-employee status provides them exceptional protections against retaliation for organizing. The absence of a legal pathway to union representation in southern states challenges labor unions to adopt new models for organizing and requires new representational structures. We review our experience as graduate employee union organizers at Indiana University during the last five years to illustrate the potential for organizing at dozens of universities throughout the South.

When theorists consider the role of effort in skill, they tend to take one of two paths. Either they argue that effort plays an important, facilitative role for skill, or they argue that effort plays a detrimental, inhibitive role for skill. I reject both accounts. At their core is what I call _consistent effort assumptions,_ or assumptions that effort plays a fixed, generalizable role in the science or metaphysics of skill. I argue that these assumptions are empirically ill-informed given that a wide body of evidence supports a varied, situation-dependent relationship between effort and skill. I offer what I take to be the proper formulation of the scientific and metaphysical relationship between effort and skill: Both across and within domains of skill, there is no consistent, generalizable relationship between effort and skill. Asserting any such relationship or tendency confuses rather than clarifies our science and metaphysics of skill. To explain the widespread proliferation of consistent effort assumptions, I consider the extent to which normative and aesthetic commitments about effort and effortlessness are well-entrenched in both intellectual and popular discourse. Though effort or effortlessness may play a role in our evaluations of skill, their normative and aesthetic features should be disentangled from our science and metaphysics of skill and its underlying cognitive mechanisms.

A crucial design feature of language useful for determining when grammatical language evolved in the human lineage is our ability to combine meaningless units to form a new unit with meaning (combinatoriality) and to further combine these meaningful units into a larger unit with a novel meaning (compositionality). There is overlap between neural bases that underlie hierarchical cognitive functions required for compositionality in both linguistic and nonlinguistic contexts (e.g., tool use). Therefore, evidence of compositional tool use in the archaeological record should signify, at the very least, the cognitive capacity for grammatical language by that point in time. We devise a novel system to evaluate the level of hierarchical nesting of tool components in single tool use activities. In the application of this system, we demonstrate that nonhuman primates, and by extension, the last common ancestor shared by Pan and Homo, are capable of basic combinatoriality; however, their technology does not approach the compositionality observable in modern human tool use. The prehistoric archaeological record supports a continuous evolution of combinatorial tool use into compositional tool use, with evidence for human-like levels of by 0.93 million years ago (Ma), or possibly as early as 1.7 Ma. While compositional language could have lagged behind compositional tool use, if language and technology coevolved according to the Technological Origin for Language hypothesis, which proposes a feedback system between the two, then evidence for hierarchical tool use structures implies a similar level of complexity in linguistic structures.