Deduction, Induction, and Hypothesis 1

Peirce, Charles S.

doi:10.4324/9781315823126-7

Cited by 56 publications

(43 citation statements)

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“…Freud, 1924;Tinbergen, 1951;Hull, 1943), did not specify enough that learning also happens in cephalic states not linked to failed expectations. Peirce (1878) was aware of this, nevertheless, and his other works emphasize this aspect of exploratory learning.…”

Section: In Reveries Of a Solitarymentioning

confidence: 91%

“…When expectations are thwarted, a broad array of learning occurs through new problem-solving and search principles. This is close in scope to Peirce's (1878) view of inquiry and the development of new solutions to problems. Of course, inquiry is more than this.…”

Section: In Reveries Of a Solitarymentioning

confidence: 95%

“…The platypus aptly illustrates some of the limits of our sampling systems (Peirce, 1878;Jastrow, 1900). The main definition of a mammal is an animal that bears live young (i.e.…”

Section: Sampling With and Without Expectationsmentioning

confidence: 98%

“…We imagine the preconditions for foraging for coherence and then place them in a sense of inquiry with constraints on hypothesis formation and inferences toward good explanation (Peirce, 1868(Peirce, , 1878, or a good enough explanation, given the context (see also Simon, 1982).…”

Section: Sampling With and Without Expectationsmentioning

confidence: 99%

“…For Leibniz, there was a whirling spatialized framework of well-ordered harmony. Anchors to objects, not sensations, were championed by investigators such as Thomas Reid. The representational capabilities are linked to action, habit, and hypothesis formation (Peirce, 1878), constrained by evolution and ecological context.…”

Section: Reflexes and Machinesmentioning

confidence: 99%

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Pragmatism and the Search for Coherence in Neuroscience

Schulkin¹

2015

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Section: In Reveries Of a Solitarymentioning

confidence: 91%

Section: In Reveries Of a Solitarymentioning

confidence: 95%

“…The platypus aptly illustrates some of the limits of our sampling systems (Peirce, 1878;Jastrow, 1900). The main definition of a mammal is an animal that bears live young (i.e.…”

Section: Sampling With and Without Expectationsmentioning

confidence: 98%

Section: Sampling With and Without Expectationsmentioning

confidence: 99%

Section: Reflexes and Machinesmentioning

confidence: 99%

See 3 more Smart Citations

Pragmatism and the Search for Coherence in Neuroscience

Schulkin¹

2015

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Supervised machine learning for theory building and testing: Opportunities in operations management

Chou

Chuang

Chou

et al. 2023

J of Ops Management

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Machine learning's (ML's) unique power to approximate functions and identify non-obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem-solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well-developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.

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Resolving governance disputes in communities: A study of software license decisions

Puranam

Shrestha

et al. 2020

Strategic Management Journal

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Research summary Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, we employ hybrid inductive methods to study discussions around initiation and change of software licenses—a fundamental and potentially contentious governance issue. First, we apply machine learning algorithms to identify robust patterns in data: resolution is more likely in larger discussion groups and in projects without a license compared to those with a license. Second, we analyze textual data to explain the causal mechanisms underpinning these patterns. The resulting theory highlights the group process (reflective agency switches disputes from bargaining to problem solving) and group property (preference alignment over attributes) that are both necessary for the resolution of governance disputes, contributing to the literature on community governance. Managerial summary Online communities play an increasingly important role in how companies innovate across organizational boundaries and attract talent across geographic locations. However, online communities are no Utopia; disputes abound even (more) when we collaborate virtually. In particular, governance disputes can threaten the functioning and existence of online communities. Our study suggests that governance disputes in online communities either unfold as bargaining over which solution is better or searching for a satisfactory solution. The latter is more likely to reach a resolution, when there is common ground. Companies interested in leveraging the power of online communities should (a) identify or train certain participants to transform endless bargaining into collective problem solving and (b) foster shared knowledge and value basis among participants through recruitment and strong organizational culture.

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Deduction, Induction, and Hypothesis 1

Cited by 56 publications

References 0 publications

Pragmatism and the Search for Coherence in Neuroscience

Pragmatism and the Search for Coherence in Neuroscience

Supervised machine learning for theory building and testing: Opportunities in operations management

Resolving governance disputes in communities: A study of software license decisions

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