Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era

George Siemens; Mihnea C. Moldoveanu

REVIEW 4 major objections 4 minor 13 references

Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Working with large language agents can be deliberately designed to develop metacognitive and meta-emotional skills, and higher education should reorganize around that insight.

desk verdict A clearly argued, self-aware position paper that names a real problem and offers a concrete blueprint, but the transfer claim at its core is asserted, not demonstrated. read the letter →

arxiv 2501.00867 v1 pith:LTTKMLQT submitted 2025-01-01 cs.HC cs.MA

Mihnea C. Moldoveanu , George Siemens This is my paper · ORCID

classification cs.HCcs.MA

keywords interactionalisminteractionalintelligencemetacognitionmeta-emotionalskillslargelanguagemodelagentsdialogicallearninghighereducationredesigngenerativeAIin

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Interactionalism argues that the rise of generative AI, especially dialogical agents built on large language models, turns the act of learning itself into a new kind of skill: interactional intelligence. The paper tries to establish that designing, prompting, testing, and refining LLM agents is not just a way to get work done but a deliberate exercise of metacognitive and meta-emotional abilities, from task decomposition to monitoring one's own objectives to inferring a user's emotional state. If that holds, universities could redesign the entire learning architecture—selection, instruction, feedback, assessment—so that the method of learning models the interactional skills graduates will need in AI-mediated workplaces. The value would be a higher education system whose process, not just its content, produces the 'meta-human' skills the labor market increasingly rewards.

What carries the argument

The load-bearing mechanism is the mapping, in the paper's tables, between a decomposition of metacognitive skills (monitoring, registering, and controlling one's own mental events) and meta-emotional and meta-relational skills (inferring and measuring emotional states, interactive reasoning), on one side, and the concrete tasks of designing LLM agents on the other. Writing and refining prompts, specifying objective functions and personas, chaining tasks, setting evaluation rubrics, and testing a user's likely responses are each claimed to require rapid switching between planning, monitoring, and control, and between cognitive and meta-cognitive frames. The complementary mechanism is the design principle that the learning experience should embody the learning objectives: interactional and dialogical skills are learned by exercising them in the fabric of the course itself.

What would settle it

Train one group to design and refine LLM agents and another on equivalent content through traditional methods, then measure both groups on established metacognitive and social-emotional instruments in human interaction tasks; if the agent-design group shows no advantage outside AI-specific tasks, the central recommendation collapses.

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Extended reading notes

Core claim

The paper's central claim is that metacognitive and meta-emotional skills—not raw recall or calculation—are the bottleneck for productive human-AI collaboration, and that these skills can be developed by engaging learners in the design and use of LLM agents. It breaks interactional intelligence into named components such as self-explicitation, task specification, task decomposition, sub-task energization, task-performance evaluation, task switching, partial-credit assignment, and objective and task refinement, then maps each onto concrete agent-design tasks, for instance specifying an agent's objective function, chaining sub-tasks, designing evaluation rubrics, and testing a user's likely responses. The same mapping is argued to hold for meta-emotional and meta-relational skills such as inferring intentionality and designing for emotional connectedness. The paper then proposes replacing individual one-shot productions like essays, calculations, models, exegeses, and verifications with evaluated transcripts of interactions with dialogical agents, and making the learning environment an always-on, agent-mediated dialogue. It explicitly does not claim to offer a theory of learning; it offers a blueprint for practice.

Load-bearing premise

The paper assumes that the metacognitive and meta-emotional moves practiced while designing and prompting LLM agents are the same skills that make people effective in human-human and human-AI work, and that practice with AI transfers to those settings; the mapping tables assert this correspondence but offer no empirical or psychometric validation.

Editorial extensions

If this is right

Higher education could shift from one-shot individual assessment to evaluation of interaction transcripts, scoring learners on the quality of their questions, challenges, and refinements rather than only on final artifacts.
Course architecture could become a network of always-on dialogical agents serving as tutor, teaching assistant, evaluator, guide, and mentor, making the large one-to-one tutoring advantage seen in classic studies achievable at scale.
Admissions and learner evaluation could include chained, multi-shot, meta-cognitive questioning that probes how a learner knows, not just what she knows.
The design goal for educational interfaces would invert: instead of reducing the metacognitive load of generative AI, educators would deliberately compose tasks that raise it as a developmental opportunity.
The skill that a degree certifies would shift from individual know-how to interactional know-how, changing what credentials signal to employers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the transfer premise holds, the same agent-design tasks could be used inside organizations as low-cost developmental exercises for the soft skills that current training programs struggle to build, turning routine AI use into upskilling.
The framework implies a testable psychometric program: build an interactional skill ontology and check whether performance on agent-design tasks predicts performance in human team settings; the paper stops at mapping tables rather than delivering such measures.
The paper's de-emphasis on 'know-what' leaves an open tension: interactional skill may depend on a base of domain knowledge, so a working redesign may need to preserve some monological content, a question the paper does not address.
A natural next experiment is to compare interaction-transcript evaluation with expert human judgment of the same artifacts to see whether transcripts carry incremental information about a learner's future workplace performance.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Punchline: this is a clearly argued, honestly scoped position paper. It identifies a real problem—GenAI breaks the assessment model of individual, monological skill demonstration—and proposes a coherent redesign built around dialogical learning with LLM agents. It is not an empirical paper, and the authors do not pretend otherwise: they state explicitly that we lack good interactional and dialogical skill ontologies and reliable measures. That admission is the paper's most useful honesty and also the hinge on which everything turns.

What is actually new is small but real. The named framework 'interactionalism' and the specific decomposition in Tables 1–3, mapping meta-cognitive and meta-emotional skill classes onto LLA design tasks such as prompt refinement, objective specification, and evaluation-rubric design, are not present in the cited literature. The underlying dialogical tradition (Vygotsky, Mercier and Sperber) is old, but the translation into concrete instructional moves for agent-work is a useful contribution, and the tables give designers something to argue with.

The soft spots are proportionate to the genre. The load-bearing claim is that practicing LLA design develops transferable 'interactional intelligence' that carries to human-human and human-AI work. That claim is asserted through the mapping tables, not tested. There is no psychometric evidence that the latent skill behind 'specifying an objective function for an LLA' is the same as the skill behind managing a human collaborator's goals. The proposed evaluation—transcripts of interaction with a dialogical agent—risks circularity: the intervention and the outcome live in the same modality, so improved transcripts could just be better prompt-formatting heuristics. The authors also cite their own prior work heavily, which is not disqualifying when the cited work is substantively relevant, but the construct 'interactional intelligence' is anchored mainly in those self-citations rather than in independent measures.

This paper deserves a serious referee. It would be a waste to desk-reject it: the argument is internally coherent, the limitations are stated in the text itself, and the design tables are concrete enough to generate testable hypotheses for learning-science and HCI researchers. A referee should require the authors to separate the programmatic vision from the empirical claims and to specify what evidence would falsify the transfer assumption. I would not cite it as evidence of transfer; I would cite it, if at all, as a representative position piece.

Referee Report

4 major / 4 minor

Summary. The paper introduces 'Interactionalism' as a set of design principles for higher education in the era of large language models (LLMs), arguing that the tasks involved in prompting, testing, and architecting LLM-based agents can develop an 'interactional intelligence' composed of metacognitive and meta-emotional skills. The authors propose restructuring learner selection, learning experiences, and evaluation around dialogical interactions with these agents, so that 'the learning experience embodies the learning objectives.' The paper is explicitly positioned not as a theory of learning but as a practical blueprint and research agenda, with conceptual decompositions of the target skills and mappings from LLM-agent design tasks to those skills.

Significance. If the central claim were established, this framework would be significant for both educational practice and human-AI interaction research: it offers a coherent vocabulary for a class of skills that many argue are increasingly valuable, and it proposes a concrete architectural response to the assessment-integrity crisis that GenAI poses to traditional education. The paper's strengths include its explicit recognition of the current absence of interactional skill ontologies and measures, its design-oriented focus on existing technologies, and its clear articulation of a research agenda. However, the manuscript currently asserts, rather than demonstrates, the load-bearing causal claim that practicing with LLM agents builds generalizable interactional skills; this is a proposal in need of empirical validation, not yet a finding.

major comments (4)

[Abstract and 'Interactional Intelligence'] The central claim that 'working with Large Language Model (LLM)-based agents can be proactively used to help develop learners' is asserted without empirical evidence or a comparison baseline. The mapping from LLA-design tasks to skill classes in Tables 2 and 3 is a plausible taxonomy but not a demonstration of construct validity or of transfer to human-human or human-AI contexts. The paper itself concedes, in the section 'But was not Cognition Always-Already Interactional?', that 'We do not have good interactional and dialogical skill ontologies – let alone measures we can reliably gauge learner progress against.' This concession undercuts the strength of the developmental claim. Please either provide evidence (even indirect) for the transfer assumption, or explicitly reframe the manuscript as a proposal with testable hypotheses and a validation plan.
[Dialogical Learning for Interactional Skill development] The proposed evaluation method, which replaces the individually produced artifact with 'a transcript of the interaction with the dialogical agent' as the basis for evaluation, risks circularity: the intervention (interacting with an LLA) and the outcome measure (a transcript of exactly that interaction) share the same modality. Improved transcripts may reflect AI-specific strategies (e.g., prompt-formatting heuristics) rather than generalizable interactional intelligence. To support the transfer claim, the paper should specify outcome measures that are external to the LLA-interaction itself, such as human-human collaboration tasks, standardized negotiation or teaching scenarios, or pre/post psychometric instruments.
[The Metacognitive and Meta-Emotional Demands... and Tables 1-3] The construct 'interactional intelligence' is not defined operationally enough to be falsifiable. The paper states that meta-cognitive and meta-emotional components 'can be understood, quantified, measured and developed' but does not provide a measurement model, item-level definitions, or reliability/validity evidence for the skill classes in Table 1 or the mappings in Tables 2 and 3. As a result, the central claim that these skills are 'articulable and measurable' (Conclusion) is circular: the authors define the construct through the tasks that allegedly exercise it, and then use those same tasks as evidence of the construct's existence. A clearer separation between the construct definition, the measurement instrument, and the intervention tasks is needed.
[Entire manuscript] The manuscript oscillates between modest disclaimers and strong causal assertions. For example, it states that Interactionalism 'is not advanced as a theory of learning' but also that the learning method 'embodies the learning objectives' and that designers of LLA tasks 'require' a 'dense and proactive exercise of meta-cognitive functions.' These claims are not inherently contradictory, but the paper does not reconcile them: if this is a blueprint, then the developmental claims should be framed as hypotheses to be tested, not as established outcomes. Please clarify the epistemic status of each major claim, especially the developmental and transfer claims.

minor comments (4)

[Abstract and throughout] There are several typographical errors that should be corrected: 'platrforms' (Abstract), 'abnd' (Section 'GenAI in Education'), 'expeirnce' (Section 'Interactional Intelligence'), 'escalatre' (Section 'But was not Cognition Always-Already Interactional?'), and 'which This closely mimics' (Section 'Learner Evaluation').
[References] The reference list is inconsistent with in-text citations. For instance, 'Johnson, Manyika and Yee, 2007' appears in text but the reference is dated 2005; 'Darvishi, et al, 2024' is cited but missing from the reference list; 'Moldoveanu and Djikic, 2017' and 'Deming, 2017' are cited but not listed; and 'Mercier and Sperber, 2012' is cited in text while the reference list gives 2011.
[Tables] Table 2's header 'Sample instance from large Language Agent Design' has inconsistent capitalization; consider harmonizing table formatting and ensuring all table entries are complete sentences.
[Introduction] The sentence beginning 'A key component of an interactionalist approach to learning is a recognition and sharp definition of dialogical agents (DA's) that capture with fidelity and nuance the conversational and interactional structures of teaching and learning' could be split for clarity, as it currently conflates the definition of dialogical agents with their role in learning.

Circularity Check

2 steps flagged · score 4.0 of 10

Conceptual blueprint with same-modality evaluation risk and load-bearing self-citation; no formal derivation reduces to inputs.

self definitional [Section 'Dialogical Learning for Interactional Skill development' (paragraph 'Crucially, interactionalism also shifts the basis for evaluation...') and Section 'Learner Evaluation']
"Crucially, interactionalism also shifts the basis for evaluation from the competent production of an artifact (an essay, a paragraph, a piece of code, an algorithm, a proof, a computation for optimizing, predicting, sorting, searching, etc) to a pattern of interaction with an appropriately patterned dialogical agent, aimed at co-producing an artifact."

The skill the paper claims to develop ('interactional intelligence') is defined as the meta-cognitive and meta-emotional capability exercised when prompting, questioning, and designing LLM-based dialogical agents. The proposed outcome measure is a transcript of interaction with a dialogical agent, i.e., the same behavior class as the intervention. By construction, then, improvement on the evaluation could reflect AI-specific prompt-formatting heuristics rather than transferable interactional skill.
self citation load bearing [Section 'GenAI in Education: The Challenge and Opportunity Set' (paragraph 'Such a learning process mirrors...')]
"The base of skills most sought after by the organizations that recruit and employ graduates has shifted towards the skills required to design and take part in interactions [Johnson, Manyika and Yee, 2007; Deming, 2015] and those skills that are predominantly communicative and dialogical in nature [Moldoveanu, 2024]."

The paper's load-bearing premise that workplace competence is now predominantly dialogical is anchored to the authors' own prior work [Moldoveanu, 2024] rather than to new data or an independent analysis presented here. The entire interactionalist redesign follows from this premise, so the self-citation is not incidental. However, the same sentence cites Deming and Johnson, Manyika and Yee, giving the labor-market shift independent partial support; this is a moderately load-bearing self-citation, not the sole basis of the argument.

full rationale

This is a conceptual blueprint rather than a formal derivation, so there is no equation-level reduction and no fitted parameter renamed as a prediction. The principal circularity risk is self-definitional: 'interactional intelligence' is defined through the very LLM-agent interaction tasks that are proposed as training, and the suggested evaluation uses transcripts of the same kind of interaction, making the outcome measure the same modality as the intervention. The paper even concedes that no reliable interactional-skill ontology or measure yet exists, reinforcing this risk. Self-citations to Moldoveanu's prior books support central premises about the dialogical nature of work, but they are supplemented by external sources such as Deming, Garicano, Bloom, Vygotsky, and Mercier and Sperber, so the framework is not wholly self-referential. Because the paper makes no empirical prediction that could be checked against an independent benchmark, the score reflects significant construct-level and self-citation circularity rather than a claim that the entire argument reduces to its own assumptions.

Assumptions & free parameters 0 free parameters · 4 assumptions · 3 invented entities

There are no numerical free parameters because the paper contains no equations or fits. The central proposal rests on several untested domain assumptions, especially the transfer of AI-interaction skills to real work, and on invented conceptual entities that lack independent measurement or falsifiable handles.

assumptions (4)

domain assumption Core cognitive tasks are automatable and augmentable by GenAI, so the binding skills become interactional and metacognitive.
Stated in the Abstract and Introduction without empirical evidence; the paper's entire agenda depends on this shift being real.
domain assumption Bloom's 2 sigma effect for human one-on-one tutoring carries over to always-on LLM-based tutoring.
Cites Bloom 1984 but does not test whether AI agents reproduce this effect; this assumption grounds the 'always-on tutor' design proposal.
domain assumption Skills practiced in interactions with LLM agents transfer to human-AI and human-human work contexts.
Tables 1-3 map LLA design tasks to skill classes but provide no transfer evidence; this is the load-bearing premise for the claimed developmental benefit.
domain assumption The labor market increasingly rewards interactional and social skills over individual cognitive skills.
Based on cited economics research (Deming, Garicano, Johnson et al); treated as settled, but not re-derived in this paper.

invented entities (3)

Interactional intelligence
purpose: A 'net new skill set' combining metacognitive, meta-emotional, and meta-relational abilities for dialogical interaction with AI agents and humans.
Defined by the authors; no psychometric measure, construct validation, or falsifiable prediction is provided in the paper.
Dialogical grammar
purpose: A proposed structure for questioning, answering, and nudging in AI-mediated learning dialogues.
The paper says such a grammar is needed but does not specify its rules or how it would be evaluated.
Meta-human skills
purpose: Higher-order skills (meta-cognitive, meta-emotional, meta-relational) that the paper says education must cultivate for AI-mediated work.
Presented as measurable ('articulable and measurable') but no instrument is given.

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Cite this review

Pith. "Pith review of Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era." pith.science (2026). https://pith.science/paper/LTTKMLQT

@misc{pith2026250100867,
  author       = {Pith},
  title        = {Pith review of: Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LTTKMLQT}},
  note         = {Machine review of arXiv:2501.00867}
}

read the original abstract

We introduce Interactionalism as a new set of guiding principles and heuristics for the design and architecture of learning now available due to Generative AI (GenAI) platforms. Specifically, we articulate interactional intelligence as a net new skill set that is increasingly important when core cognitive tasks are automatable and augmentable by GenAI functions. We break down these skills into core sets of meta-cognitive and meta-emotional components and show how working with Large Language Model (LLM)-based agents can be proactively used to help develop learners. Interactionalism is not advanced as a theory of learning; but as a blueprint for the practice of learning - in coordination with GenAI.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 9 canonical work pages

[1]

The learning experience embodies the learning objectives

INTERACTIONALISM 1 Interactionalism1: Re-Designing Higher Learning for the Large Language Agent Era Mihnea Moldoveanu* and George Siemens** 31 December 2024 *University of Toronto: mihnea.moldoveanu@rotman.utoronto.ca **Matter and Space, Inc.: gsiemens@matterandspace.com Abstract We introduce Interactionalism as a new set of guiding principles and heurist...

work page 2024
[2]

Mapping of meta-cognitive tasks requiring meta-cognitive skills to the tasks interactionalist models of learning and work entails. In addition to the meta-cognitive dimension of competence, designing, testing, evaluating, deploying and refining Large Language Agents also taps into a meta-emotional and meta-relational dimension of human capabilities. Simpl...

work page 2022
[3]

Dialing up Meta-Skills to the Meta-Emotional and Meta-Relational Realms Far from introducing a need to ‘simplify’ or ‘dumb down’ the process by which humans interact with Large Language Models, the new learning landscape enabled by the need for Large Language Agents and people that can pattern, create, deploy, test and refine them reveals a new frontier o...

work page 2023
[7]

Behavioral and Brain Sciences

Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences. 34, 57 –111. doi:10.1017/S0140525X10000968 Moldoveanu, M.C. and Martin, R.L

doi:10.1017/s0140525x10000968
[12]

Journal of the International Neuropsychological Society

Functions of the frontal lobes: relation to executive functions. Journal of the International Neuropsychological Society. 17, 759–765. doi: 10.1017/S1355617711000695. Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A.E., Sarkar, A., Sellen, A., and Rintel, S

work page doi:10.1017/s1355617711000695
[13]

In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024)

The Metacognitive Demands and Opportunities of Generative AI. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024). Taylor, J. E. T., & Taylor, G. W. (2021). Artificial cognition: How experimental psychology can help generate explainable artificial intelligence. Psychonomic Bulletin & Review, 28(2), 454-475 Tversky, A. and...

work page 2021
[1984]

Educational Researcher

The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher. 13, 4-16. Bloor, D. (1983) Wittgenstein: A social theory of knowledge. New York: Columbia University Press Borghans, L., Duckworth, A. L., Heckman, J. J., and ter Weel, B

work page 1983
[2000]

Journal of Political Economy, 108(5), 874–904

Hierarchies and the Organization of Knowledge in Production. Journal of Political Economy, 108(5), 874–904. https://doi.org/10.1086/317671 Gigerenzer, G. and Gaissmaier, W

doi:10.1086/317671

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[2005]

International Journal of Instructional Technology and Distance Learning, 2(1) (January, 2005)

Connectivism: A Learning Theory for the Digital Age. International Journal of Instructional Technology and Distance Learning, 2(1) (January, 2005). Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S. and de Laat, M

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[2011]

Annual Review of Psychology, 62, 451-482

Heuristic Decision Making. Annual Review of Psychology, 62, 451-482. https://doi.org/10.1146/annurev-psych-120709-145346 Goodfellow, I., Bengio, Y., and Courville, A

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[2022]

What is it like to program with artificial intelligence? Arxiv: 2208.06213

arXiv
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Should Computers Be Easy To Use? Questioning the Doctrine of Simplicity in User Interface Design. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, 1–10. Siemens, G

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Co-intelligence: Living and working with AI. WH Allen. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486 Sarkar, A. D. Gordon, C. Negreanu, C. Poelitz, S.S. Ragavan, and B. Zorn

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

The learning experience embodies the learning objectives

INTERACTIONALISM 1 Interactionalism1: Re-Designing Higher Learning for the Large Language Agent Era Mihnea Moldoveanu* and George Siemens** 31 December 2024 *University of Toronto: mihnea.moldoveanu@rotman.utoronto.ca **Matter and Space, Inc.: gsiemens@matterandspace.com Abstract We introduce Interactionalism as a new set of guiding principles and heurist...

work page 2024

[2] [2]

Mapping of meta-cognitive tasks requiring meta-cognitive skills to the tasks interactionalist models of learning and work entails. In addition to the meta-cognitive dimension of competence, designing, testing, evaluating, deploying and refining Large Language Agents also taps into a meta-emotional and meta-relational dimension of human capabilities. Simpl...

work page 2022

[3] [3]

Dialing up Meta-Skills to the Meta-Emotional and Meta-Relational Realms Far from introducing a need to ‘simplify’ or ‘dumb down’ the process by which humans interact with Large Language Models, the new learning landscape enabled by the need for Large Language Agents and people that can pattern, create, deploy, test and refine them reveals a new frontier o...

work page 2023

[4] [7]

Behavioral and Brain Sciences

Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences. 34, 57 –111. doi:10.1017/S0140525X10000968 Moldoveanu, M.C. and Martin, R.L

doi:10.1017/s0140525x10000968

[5] [12]

Journal of the International Neuropsychological Society

Functions of the frontal lobes: relation to executive functions. Journal of the International Neuropsychological Society. 17, 759–765. doi: 10.1017/S1355617711000695. Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A.E., Sarkar, A., Sellen, A., and Rintel, S

work page doi:10.1017/s1355617711000695

[6] [13]

In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024)

The Metacognitive Demands and Opportunities of Generative AI. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 2024). Taylor, J. E. T., & Taylor, G. W. (2021). Artificial cognition: How experimental psychology can help generate explainable artificial intelligence. Psychonomic Bulletin & Review, 28(2), 454-475 Tversky, A. and...

work page 2021

[7] [1984]

Educational Researcher

The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher. 13, 4-16. Bloor, D. (1983) Wittgenstein: A social theory of knowledge. New York: Columbia University Press Borghans, L., Duckworth, A. L., Heckman, J. J., and ter Weel, B

work page 1983

[8] [2000]

Journal of Political Economy, 108(5), 874–904

Hierarchies and the Organization of Knowledge in Production. Journal of Political Economy, 108(5), 874–904. https://doi.org/10.1086/317671 Gigerenzer, G. and Gaissmaier, W

doi:10.1086/317671