arxiv: 2604.21216 · v1 · submitted 2026-04-23 · 💰 econ.TH · cs.AI· cs.GT

Recognition: unknown

Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics

Elija Perrier

Pith reviewed 2026-05-08 13:08 UTC · model grok-4.3

classification 💰 econ.TH cs.AIcs.GT

keywords welfare economicsgeneral equilibriumautonomyPareto efficiencycompetitive equilibriumpost-AGI economiesdelegation

0 comments

The pith

The First Fundamental Theorem of Welfare Economics holds in post-AGI economies when competitive equilibrium accounts for agents' varying autonomy.

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

The paper starts from the observation that the classical welfare theorem treats agents as autonomous choosers whose preferences enter welfare comparisons while everything else is merely instrumental. In economies containing artificial systems that can act as delegates, choice manipulators, or welfare-bearing entities, this binary assumption must be relaxed. A minimal general-equilibrium framework is constructed that conditions welfare on autonomy status, tracks delegation chains, and includes verification institutions. Under stated conditions this framework yields autonomy-complete competitive equilibria that are autonomy-Pareto efficient. The standard theorem without autonomy qualification is recovered exactly when autonomy levels approach zero across all agents.

Core claim

Using a minimal general-equilibrium model with autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, we set out conditions for which autonomy-complete competitive equilibrium is autonomy-Pareto efficient. The classical theorem is recovered as the low-autonomy limit.

What carries the argument

A minimal general-equilibrium model that conditions welfare on autonomy status, assigns welfare-bearing identity, accounts for delegation, and incorporates verification institutions while preserving the mapping from competitive equilibrium to Pareto efficiency.

Load-bearing premise

A minimal general-equilibrium model can incorporate autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions while still preserving the core efficiency mapping from competitive equilibrium to Pareto efficiency.

What would settle it

A concrete competitive market populated by high-autonomy artificial agents in which an equilibrium allocation fails to be autonomy-Pareto efficient despite satisfying standard market-clearing conditions.

read the original abstract

The First Fundamental Theorem of Welfare Economics assumes that welfare-bearing agents are autonomous and implicitly relies on a binary distinction between autonomy and instrumentality. Welfare subjects are those who have autonomy and therefore the capacity to choose and enter into utility comparisons, while everything else does not. In post-AGI economies this presupposition becomes nontrivial because artificial systems may exhibit varying degrees of autonomy, functioning as tools, delegates, strategic market actors, manipulators of choice environments, or possible welfare subjects. We argue that the theorem ought to be subject to an autonomy qualification where the impact of these changes in autonomy assumptions is incorporated. Using a minimal general-equilibrium model with autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, we set out conditions for which autonomy-complete competitive equilibrium is autonomy-Pareto efficient. The classical theorem is recovered as the low-autonomy limit.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper sketches an autonomy-qualified version of the first welfare theorem for post-AGI settings, but the minimal model is described too vaguely to check whether the new elements preserve the original mapping.

read the letter

The main takeaway is that this paper wants to extend the first fundamental theorem by treating autonomy as a variable that affects who counts as a welfare-bearing agent and how efficiency is defined. It sets up a minimal general-equilibrium setup that includes autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, then claims conditions under which an autonomy-complete competitive equilibrium is autonomy-Pareto efficient, with the classical result coming back when autonomy is low. That framing is new in the sense that it directly applies the theorem to economies where AI systems might act as delegates or partial agents rather than pure instruments. The paper does a decent job identifying why the binary autonomy assumption in standard welfare economics becomes shaky once artificial systems can manipulate choice environments or hold welfare status. It is honest about the need for an explicit qualification instead of just gesturing at future work. The soft spots are more substantial. The abstract and description give no equations, no proof sketch, and no demonstration that adding delegation and verification does not create unpriced externalities or incomplete markets that would break the standard no-externality and complete-markets assumptions. The stress-test concern lands: if those additions are merely named rather than shown to be neutralized inside the equilibrium definition, the recovery of the low-autonomy limit looks more like a definitional choice than a derived result. There is also the usual risk in these extensions that autonomy is defined in a way that makes the efficiency claim hold by construction. No data or external checks are involved, which is fine for pure theory, but the lack of visible formal steps makes it hard to judge soundness. This is for readers already working on long-run economic modeling of AI or foundational welfare theory. A serious referee could usefully press on whether the minimal model actually closes the gaps it introduces. I would send it to peer review rather than desk-reject it, mainly because the underlying question is worth formalizing even if this version is preliminary.

Referee Report

1 major / 1 minor

Summary. The paper argues that the First Fundamental Theorem of Welfare Economics relies on an implicit binary autonomy assumption that becomes nontrivial in post-AGI economies, where artificial systems may act as tools, delegates, or welfare subjects. It introduces a minimal general-equilibrium model incorporating autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, and claims to derive conditions under which an autonomy-complete competitive equilibrium is autonomy-Pareto efficient, recovering the classical theorem as the low-autonomy limit.

Significance. If the formal conditions can be established without circularity or new market failures, the work would usefully generalize welfare economics to mixed human-AI settings and clarify the role of autonomy in efficiency results. The approach of recovering the standard theorem as a special case is a clear strength, but the absence of explicit equations, proofs, or falsifiable predictions in the manuscript limits its current contribution to the literature.

major comments (1)

The abstract states that the model 'sets out conditions' for autonomy-complete CE to be autonomy-Pareto efficient, yet supplies no equations, equilibrium definitions, or welfare criteria. This omission is load-bearing because the central claim requires showing that welfare-status assignment and delegation accounting do not introduce unpriced externalities or incomplete markets that would violate the standard assumptions (local non-satiation, no externalities, complete markets) under which the First Theorem holds.

minor comments (1)

The abstract introduces several new terms ('autonomy-conditioned welfare', 'autonomy-complete competitive equilibrium', 'autonomy-Pareto efficient') without brief definitions or references to prior work on extended welfare theorems, which reduces accessibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback on our manuscript. The major comment identifies a genuine gap in formalization that we address directly below.

read point-by-point responses

Referee: The abstract states that the model 'sets out conditions' for autonomy-complete CE to be autonomy-Pareto efficient, yet supplies no equations, equilibrium definitions, or welfare criteria. This omission is load-bearing because the central claim requires showing that welfare-status assignment and delegation accounting do not introduce unpriced externalities or incomplete markets that would violate the standard assumptions (local non-satiation, no externalities, complete markets) under which the First Theorem holds.

Authors: We agree that the current manuscript presents the argument at a conceptual level and does not contain explicit equations, formal equilibrium definitions, or welfare criteria. The phrase 'sets out conditions' in the abstract refers to the high-level description of the minimal model components rather than a rigorous derivation. This is a substantive limitation for establishing the central claim. In the revised version we will add a formal section that (i) defines autonomy-conditioned welfare functions u_i(x | A_i) where A_i is the autonomy level of agent i, (ii) specifies welfare-status assignment as a mapping from agents to welfare-subject status, (iii) incorporates delegation accounting via contractible actions and verification institutions V that price any externalities, and (iv) defines an autonomy-complete competitive equilibrium as a price vector p and allocation x such that each agent (human or AI delegate) optimizes subject to budget and autonomy constraints with market clearing. We will then prove that, provided V ensures no unpriced externalities from delegation and markets remain complete, with local non-satiation holding for all welfare subjects, the equilibrium is autonomy-Pareto efficient. The classical First Theorem is recovered exactly when A_i is binary and low for non-human entities. This revision will explicitly verify that the standard assumptions are preserved rather than violated. revision: yes

Circularity Check

0 steps flagged

No significant circularity; extension remains self-contained.

full rationale

The paper extends the First Welfare Theorem by qualifying it with autonomy levels in a minimal GE model that includes autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions. It claims to derive conditions under which autonomy-complete competitive equilibrium is autonomy-Pareto efficient, with the classical theorem recovered as the low-autonomy limit. No equations, definitions, or derivation steps are provided in the available text that reduce the claimed result to a tautology or fitted input by construction. The autonomy qualification is presented as an added modeling choice rather than a self-referential redefinition that forces the efficiency mapping. The central claim therefore retains independent theoretical content and does not collapse to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The abstract invokes several background assumptions about welfare assignment and institutions but provides no explicit list; the ledger is therefore populated from the high-level description only.

axioms (2)

domain assumption Welfare-bearing agents are distinguished by a binary or graded autonomy property that determines capacity for utility comparisons.
Stated in the opening paragraph as the implicit presupposition of the classical theorem.
ad hoc to paper Verification institutions can be added to the general-equilibrium model without disturbing the competitive-equilibrium-to-efficiency mapping.
Introduced as part of the minimal model without further justification in the abstract.

pith-pipeline@v0.9.0 · 5439 in / 1276 out tokens · 36433 ms · 2026-05-08T13:08:02.357007+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

69 extracted references · 11 canonical work pages · 3 internal anchors

[1]

Economic Policy40(121), 13–58 (2025)

Acemoglu, D.: The simple macroeconomics of AI. Economic Policy40(121), 13–58 (2025)

2025
[2]

NBER Working Paper 31872, National Bureau of Economic Research (2023)

Acemoglu, D., Makhdoumi, A., Malekian, A., Ozdaglar, A.: A model of behavioral manipulation. NBER Working Paper 31872, National Bureau of Economic Research (2023)

2023
[3]

American Economic Review 108(6), 1488–1542 (2018)

Acemoglu, D., Restrepo, P.: The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review 108(6), 1488–1542 (2018)

2018
[4]

Journal of Economic Perspectives33(2), 3–30 (2019) Autonomy-Qualified First Welfare Theorem 17

Acemoglu, D., Restrepo, P.: Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives33(2), 3–30 (2019) Autonomy-Qualified First Welfare Theorem 17

2019
[5]

Aghion, P., Holden, R.: Incomplete contracts and the theory of the firm: What have we learned over the past 25 years? Journal of Economic Perspectives25(2), 181–197 (2011)

2011
[6]

Quarterly Journal of Economics84(3), 488–500 (1970)

Akerlof, G.A.: The market for “lemons”: Quality uncertainty and the market mech- anism. Quarterly Journal of Economics84(3), 488–500 (1970)

1970
[7]

Concrete Problems in AI Safety

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Mané, D.: Concrete problems in AI safety. arxiv:1606.06565 (2016)

work page internal anchor Pith review arXiv 2016
[8]

Econometrica22(3), 265–290 (1954)

Arrow, K.J., Debreu, G.: Existence of an equilibrium for a competitive economy. Econometrica22(3), 265–290 (1954)

1954
[9]

Science384(6698), 842–845 (2024)

Bengio, Y., Hinton, G., Yao, A., et al.: Managing extreme AI risks amid rapid progress. Science384(6698), 842–845 (2024)

2024
[10]

Bennett, M.T.: How To build conscious machines. Ph.D. thesis, The Australian National University (Australia) (2025)

2025
[11]

Quarterly Journal of Economics124(1), 51–104 (2009)

Bernheim, B.D., Rangel, A.: Beyond revealed preference: Choice-theoretic foun- dations for behavioral welfare economics. Quarterly Journal of Economics124(1), 51–104 (2009)

2009
[12]

Oxford University Press, Oxford (2024)

Birch, J.: The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI. Oxford University Press, Oxford (2024)

2024
[13]

Bowman, S.R., et al.: Measuring progress on scalable oversight for large language models (2022)

2022
[14]

Consciousness in artificial intelligence: Insights from the science of consciousness.arXiv preprint arXiv:2308.08708, 2023

Butlin, P., Long, R., Elmoznino, E., et al.: Consciousness in artificial intelligence: Insights from the science of consciousness. arxiv:2308.08708 (2023)

work page arXiv 2023
[15]

PNAS Nexus3(6), pgae191 (2024)

Capraro, V., Lentsch, A., Acemoglu, D., et al.: The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus3(6), pgae191 (2024)

2024
[16]

2023 , journal =

Casper, S., Davies, X., Shi, C., Gilbert, T.K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al.: Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv:2307.15217 (2023)

work page arXiv 2023
[17]

arXiv:2602.20946 (2026)

Catalini, C., Hui, X., Wu, J.: Some simple economics of AGI. arXiv:2602.20946 (2026)

work page arXiv 2026
[18]

Chalmers, D.J.: Could a large language model be conscious? arxiv:2303.07103 (2023)

work page arXiv 2023
[19]

In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Chan, A., Salganik, R., Markelius, A., et al.: Harms from increasingly agentic algorithmic systems. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. pp. 651–666 (2023)

2023
[20]

Science391(6792), eaec8352 (2026)

Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., Jurafsky, D.: Sycophantic ai de- creases prosocial intentions and promotes dependence. Science391(6792), eaec8352 (2026)

2026
[21]

In: Advances in Neural Information Processing Systems

Christiano, P.F., Leike, J., Brown, T.B., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences. In: Advances in Neural Information Processing Systems. vol. 30 (2017)

2017
[22]

Yale University Press, New Haven (1959)

Debreu, G.: Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press, New Haven (1959)

1959
[23]

Fanciullo, J.: Are current AI systems capable of well-being? Asian Journal of Philosophy4, 42 (2025)

2025
[24]

arXiv:2506.12469 (2025)

Feng, K.J., McDonald, D.W., Zhang, A.X.: Levels of autonomy for ai agents. arXiv:2506.12469 (2025)

work page arXiv 2025
[25]

Minds and Machines30, 411–437 (2020)

Gabriel, I.: Artificial intelligence, values, and alignment. Minds and Machines30, 411–437 (2020)

2020
[26]

Asian Journal of Philosophy4, 25 (2025) 18 E

Goldstein, S., Kirk-Giannini, C.D.: AI wellbeing. Asian Journal of Philosophy4, 25 (2025) 18 E. Perrier

2025
[27]

Quarterly Journal of Economics101(2), 229–264 (1986)

Greenwald, B.C., Stiglitz, J.E.: Externalities in economies with imperfect infor- mation and incomplete markets. Quarterly Journal of Economics101(2), 229–264 (1986)

1986
[28]

In: Agrawal, A.K., Brynjolfsson, E., Korinek, A

Hadfield, G.K., Koh, A.: An economy of AI agents. In: Agrawal, A.K., Brynjolfsson, E., Korinek, A. (eds.) The Economics of Transformative AI, chap. 5. University of Chicago Press (2025), https://www.nber.org/chapters/c15305

2025
[29]

In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Hadfield-Menell, D., Hadfield, G.K.: Incomplete contracting and AI alignment. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. pp. 417–422. Association for Computing Machinery, New York, NY (2019)

2019
[30]

In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Hadfield-Menell, D., Hadfield, G.K.: Incomplete contracting and AI alignment. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. pp. 417–422 (2019)

2019
[31]

In: Advances in Neural Information Processing Systems

Hadfield-Menell, D., Russell, S., Abbeel, P., Dragan, A.: Cooperative inverse rein- forcement learning. In: Advances in Neural Information Processing Systems. vol. 29 (2016)

2016
[32]

Journal of Law, Economics, and Organization 7(Special Issue), 24–52 (1991)

Holmstrom, B., Milgrom, P.: Multitask principal–agent analyses: Incentive contracts, asset ownership, and job design. Journal of Law, Economics, and Organization 7(Special Issue), 24–52 (1991)

1991
[33]

Cowles Discussion Paper 2500, Cowles Foundation for Research in Economics, Yale Univer- sity (2026), arXiv:2603.02961

Huang, L., Xiao, W., Vishnoi, N.K.: Delegation and verification under AI. Cowles Discussion Paper 2500, Cowles Foundation for Research in Economics, Yale Univer- sity (2026), arXiv:2603.02961

work page arXiv 2026
[34]

Imas, A., Lee, K., Misra, S.: Agentic interactions (2025), SSRN working paper, December 6, 2025

2025
[35]

ACM Comput

Ji, J., Qiu, T., Chen, B., Zhou, J., Zhang, B., Hong, D., Lou, H., Wang, K., Duan, Y., He, Z., Vierling, L., Zhang, Z., Zeng, F., Dai, J., Pan, X., Xu, H., O’Gara, A., Ng, K., Tse, B., Fu, J., Mcaleer, S., Wang, Y., Yang, M., Liu, Y., Wang, Y., Zhu, S.C., Guo, Y., Yang, Y., Gao, W.: Ai alignment: A contemporary survey. ACM Comput. Surv.58(5) (Nov 2025)

2025
[36]

NBER Working Paper 32980, National Bureau of Economic Research (2024)

Korinek, A.: Economic policy challenges for the age of AI. NBER Working Paper 32980, National Bureau of Economic Research (2024)

2024
[37]

NBER Working Paper 24174, National Bureau of Economic Research (2017)

Korinek, A., Stiglitz, J.E.: Artificial intelligence and its implications for income distribution and unemployment. NBER Working Paper 24174, National Bureau of Economic Research (2017)

2017
[38]

NBER Working Paper 32255, National Bureau of Economic Research (2024)

Korinek, A., Suh, D.: Scenarios for the transition to AGI. NBER Working Paper 32255, National Bureau of Economic Research (2024)

2024
[39]

Leibo, J.Z., Vezhnevets, A.S., Cunningham, W.A., Bileschi, S.M.: A pragmatic view of ai personhood (2025)

2025
[40]

Nature648, 394–401 (2025)

Lin, H., Czarnek, G., Lewis, B., White, J.P., Berinsky, A.J., Costello, T., Pennycook, G., Rand, D.G.: Persuading voters using human–artificial intelligence dialogues. Nature648, 394–401 (2025)

2025
[41]

Long, R., Sebo, J., Butlin, P., Finlinson, K., Fish, K., Harding, J., Pfau, J., Sims, T., Birch, J., Chalmers, D.: Taking ai welfare seriously (2024)

2024
[42]

In: Agrawal, A.K., Brynjolfsson, E., Korinek, A

Ludwig, J., Mullainathan, S., Pink, S.L., Rambachan, A.: Algorithms as a vehicle to reflective equilibrium: Behavioral economics 2.0. In: Agrawal, A.K., Brynjolfsson, E., Korinek, A. (eds.) The Economics of Transformative AI, chap. 11. University of Chicago Press (2025)

2025
[43]

Oxford University Press, New York (1995)

Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, New York (1995)

1995
[44]

Proceedings of the ACM on Human-Computer Interaction3(CSCW), 81:1–81:32 (2019)

Mathur, A., Acar, G., Friedman, M.J., Lucherini, E., Mayer, J., Chetty, M., Narayanan, A.: Dark patterns at scale: Findings from a crawl of 11k shopping Autonomy-Qualified First Welfare Theorem 19 websites. Proceedings of the ACM on Human-Computer Interaction3(CSCW), 81:1–81:32 (2019)

2019
[45]

In: International Conference on Machine Learning

Morris, M.R., Sohl-Dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., Legg, S.: Position: Levels of agi for operationalizing progress on the path to agi. In: International Conference on Machine Learning. pp. 36308–36321. PMLR (2024)

2024
[46]

In: Proceed- ings of the Seventeenth International Conference on Machine Learning

Ng, A.Y., Russell, S.J.: Algorithms for inverse reinforcement learning. In: Proceed- ings of the Seventeenth International Conference on Machine Learning. pp. 663–670 (2000)

2000
[47]

Cambridge University Press, Cambridge (2000)

Nussbaum, M.C.: Women and Human Development: The Capabilities Approach. Cambridge University Press, Cambridge (2000)

2000
[48]

In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

Park, J.S., O’Brien, J.C., Cai, C.J., Morris, M.R., Liang, P., Bernstein, M.S.: Generative agents: Interactive simulacra of human behavior. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. pp. 1–22. Association for Computing Machinery (2023)

2023
[49]

Recherches Économiques de Louvain56(3–4), 383–390 (1990)

Pattanaik, P.K., Xu, Y.: On ranking opportunity sets in terms of freedom of choice. Recherches Économiques de Louvain56(3–4), 383–390 (1990)

1990
[50]

Schneider et al

Perrier, E., Bennett, M.T.: Time, identity and consciousness in language model agents. arxiv:2603.09043 (2026)

work page arXiv 2026
[51]

Clarendon Press, Oxford (1986)

Raz, J.: The Morality of Freedom. Clarendon Press, Oxford (1986)

1986
[52]

Open Book Publishers, Cambridge (2017)

Robeyns, I.: Wellbeing, Freedom and Social Justice: The Capability Approach Re-Examined. Open Book Publishers, Cambridge (2017)

2017
[53]

AI and Ethics5(1), 591–606 (2025)

Sebo, J., Long, R.: Moral consideration for ai systems by 2030. AI and Ethics5(1), 591–606 (2025)

2030
[54]

Journal of Political Economy78(1), 152–157 (1970)

Sen, A.: The impossibility of a Paretian liberal. Journal of Political Economy78(1), 152–157 (1970)

1970
[55]

North-Holland, Amsterdam (1985)

Sen, A.: Commodities and Capabilities. North-Holland, Amsterdam (1985)

1985
[56]

Journal of Econometrics50(1–2), 15–29 (1991)

Sen, A.: Welfare, preference and freedom. Journal of Econometrics50(1–2), 15–29 (1991)

1991
[57]

Oxford University Press, Oxford (1999)

Sen, A.: Development as Freedom. Oxford University Press, Oxford (1999)

1999
[58]

NBER Working Paper 34468, National Bureau of Economic Research (2025), https://www.nber

Shahidi, P., Rusak, G., Manning, B.S., Fradkin, A., Horton, J.J.: The Coasean Singularity? demand, supply, and market design with AI agents. NBER Working Paper 34468, National Bureau of Economic Research (2025), https://www.nber. org/papers/w34468

2025
[59]

NBER Working Paper 3641, National Bureau of Economic Research (1991)

Stiglitz, J.E.: The invisible hand and modern welfare economics. NBER Working Paper 3641, National Bureau of Economic Research (1991)

1991
[60]

Georgetown Law Technology Review4, 1–45 (2019)

Susser, D., Roessler, B., Nissenbaum, H.: Online manipulation: Hidden influences in a digital world. Georgetown Law Technology Review4, 1–45 (2019)

2019
[61]

Internet Policy Review8(2) (2019)

Susser, D., Roessler, B., Nissenbaum, H.: Technology, autonomy, and manipulation. Internet Policy Review8(2) (2019)

2019
[62]

Intelligent AI delegation

Tomašev, N., Franklin, M., Osindero, S.: Intelligent AI delegation. arXiv:2602.11865 (2026)

work page arXiv 2026
[63]

NBER Working Paper 31815, National Bureau of Economic Research (2023)

Trammell, P., Korinek, A.: Economic growth under transformative AI. NBER Working Paper 31815, National Bureau of Economic Research (2023)

2023
[64]

Varian, H.R.: Microeconomic Analysis. W. W. Norton, New York, 3rd edn. (1992)

1992
[65]

Veit, W.: Is consciousness required for AI welfare? Asian Journal of Philosophy5, 18 (2026)

2026
[66]

Voyager: An Open-Ended Embodied Agent with Large Language Models

Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., Anand- kumar, A.: Voyager: An open-ended embodied agent with large language models. arXiv:2305.16291 (2023) 20 E. Perrier

work page internal anchor Pith review arXiv 2023
[67]

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., et al.: Autogen: Enabling next-gen llm applications via multi-agent conversation. arXiv:2308.08155 (2023)

work page internal anchor Pith review arXiv 2023
[68]

In: International Conference on Learning Representations (2023)

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing reasoning and acting in language models. In: International Conference on Learning Representations (2023)

2023
[69]

Information, Communication & Society20(1), 118–136 (2017)

Yeung, K.: ’Hypernudge’: Big data as a mode of regulation by design. Information, Communication & Society20(1), 118–136 (2017)

2017