Organizations as Artificial Intelligences: The Use of Artificial Intelligence Analogies in Organization Theory

Csaszar, Felipe A.; Steinberger, Thomas

doi:10.5465/annals.2020.0192

Cited by 24 publications

(6 citation statements)

References 188 publications

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“…Scholars have studied AI agents and familiarity ( Ahn et al, 2021 , 2022 ; Li and Sung, 2021 ; Baek et al, 2022 ). Studies assume that artificial agents can behave in ways that are reliably similar to how humans behave ( Fiore and Wiltshire, 2016 ; Jarrahi, 2018 ; Csaszar and Steinberger, 2022 ). 3 In one design, a record of each artificial agent’s experiences was connected to a large AI language model, giving the artificial agent the ability to synthesize its memories over time into higher level reflections and then to retrieve them, a result similar to human planning behavior.…”

Section: The Metaverse and Familiaritymentioning

confidence: 99%

Fluid teams in the metaverse: exploring the (un)familiar

Jarvenpaa,

Keating

2024

Front. Psychol.

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The metaverse is a new and evolving environment for fluid teams and their coordination in organizations. Fluid teams may have no prior familiarity with each other or working together. Yet fluid teams are known to benefit from a degree of familiarity–knowledge about teams, members, and working together–in team coordination and performance. The metaverse is unfamiliar territory that promises fluidity in contexts–seamless traversal between physical and virtual worlds. This fluidity in contexts has implications for familiarity in interaction, identity, and potentially time. We explore the opportunities and challenges that the metaverse presents in terms of (un)familiarity. Improved understandings of (un)familiarity may pave the way for new forms of fluid team experiences and uses.

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Section: The Metaverse and Familiaritymentioning

confidence: 99%

Fluid teams in the metaverse: exploring the (un)familiar

Jarvenpaa,

Keating

2024

Front. Psychol.

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“…In human cognition, a wealth of research supports the interdependent roles of memory, attention, and reasoning in supporting intelligence, such as work on the developmental cascades observed in normal child development, in which gains in attentional control enable advances in working memory, reasoning, and problem-solving (Fry & Hale, 1996;Masten & Cicchetti, 2010;Tourva & Spanoudis, 2020). Memory, attention, and reasoning functions also play a central role in work on AI, in which learned patterns stored in memory guide attention and enable adaptive reasoning and learning (Csaszar & Steinberger, 2022;Gugerty, 2006;Hawkins & Blakeslee, 2004;Young & Lewis, 1999). Indeed, many are surprised to learn that the concept of "working memory," commonly associated with human cognition, was first introduced in work on AI by Newell and Simon (Miller et al, 1960;Newell & Simon, 1956) and only later generalized to psychology (Baddeley & Hitch, 1974), now recognized as playing a significant role in human intelligence (P. L. Ackerman et al, 2005).…”

Section: From Individual Intelligence To CImentioning

confidence: 99%

Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

Woolley,

Gupta

2023

Perspect Psychol Sci

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As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions—an ability we define as “collective intelligence.” We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.

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“…The idea that entrepreneurs and other economic actors are theorists and scientists both relates to and differs from other concepts such as strategic foresight and representation (Csaszar, 2018; Csaszar & Levinthal, 2016; Csaszar & Steinberger, 2022; Peterson & Wu, 2021), entrepreneurial judgment (Foss & Klein, 2012; Packard et al, 2017), entrepreneurial choice (Agrawal et al, 2021; Gans et al, 2019), and entrepreneurial heuristics (Bingham & Eisenhardt, 2011; Mousavi & Gigerenzer, 2014). Representations in the neo‐Carnegie tradition are commonly cast as lower dimensional or reduced‐form depictions of a performance landscape or environment (Gavetti & Levinthal, 2000, p. 135; also see Gavetti & Menon, 2016) or as a simplified understanding of the reality individuals face (Gavetti, 2012; Lakoff, 1987).…”

Section: The Theory Of Value Approach In Entrepreneurshipmentioning

confidence: 99%

The theory‐based view: Entrepreneurial microfoundations, resources, and choices

Wuebker

Zenger

Felin

2023

Strategic Management Journal

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Research SummaryWe build on the theory‐based view and highlight how an entrepreneur's theory can enable the creation of value. We point to three types of theories: theories of resource arbitrage, resource recombination, or resource investment that specialize resources to particular uses. However, possessing a unique theory is not enough. The realization of an entrepreneur's theory is contingent on matching it with the right organizational and governance‐related choices. Entrepreneurs must make consequential decisions about protecting intellectual property, running experiments, acquiring resources, composing teams, and securing financing. We develop a contingent approach that enables entrepreneurs to match their theory of value with the appropriate forms of action, thereby providing entrepreneurs with a level of guidance to downstream choices that the current literature fails to provide.Managerial SummaryTo create economic value, entrepreneurs need to develop their own, unique theory of value. In this article, we highlight three distinct types of entrepreneurial theory and link these to value creation. We further point out how the realization of each theory of value depends on making the right downstream choices related to the protection of intellectual property, experimentation, resource acquisition, the composition of a team, and financing. Existing advice to entrepreneurs tends to offer one‐size‐fits‐all solutions (e.g., related to experimentation), while we argue that choices about how to pursue value creation should depend on the type of theory of value that an entrepreneur pursues.

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Organizations as Artificial Intelligences: The Use of Artificial Intelligence Analogies in Organization Theory

Cited by 24 publications

References 188 publications

Fluid teams in the metaverse: exploring the (un)familiar

Fluid teams in the metaverse: exploring the (un)familiar

Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

The theory‐based view: Entrepreneurial microfoundations, resources, and choices

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