Where the Ideas Came From

This page goes with the ICIS 2026 frieze. Along its top edge runs a history of the ideas behind today’s AI, in four rows: neural networks; symbolic AI, search and evolution; learning from reward; and organizations and systems, which is where the information systems literature sits.

Each arc joins a later work to an earlier one whose idea it took up. A red arc means the later work used the idea. A blue arc means it explained it, and a black arc means each work drew on the other. A green arc means the later work answered an objection to the earlier one, and a violet arc means it read the earlier idea as the same thing. A grey dashed arc means the idea has come back without yet being put to use. The history is not a straight line of progress. Some ideas were set aside for decades, then came back and became central. Search, from 1956, is used again in AlphaGo and in coding agents. Associative memory, from 1982, is used again in attention. The law of effect, from 1911, is the basis of reinforcement learning.

The same pattern appears in information systems research. Sarker and colleagues (2019) brought the field back to the socio-technical systems of Trist and Bamforth (1951). Abbasi and colleagues (2024) based their proposals for design research on AI on Hevner and colleagues (2004), who drew on Simon's The Sciences of the Artificial (1969).

The bottom row also holds work on three changes that IS research studies closely. The first is work and daily life being recorded as data (Zuboff, 1988 and 2019; Mayer-Schönberger and Cukier, 2013). The second is platforms taking over markets and the organisation of work (Tiwana and colleagues, 2010; Helmond, 2015; de Reuver and colleagues, 2018). The third is algorithms directing workers (Faraj and colleagues, 2018; Kellogg and colleagues, 2020; Möhlmann and colleagues, 2021). Two arcs run from these changes into the history of AI. Crowd workers on an online platform labelled the images on which deep learning was trained. The data that information systems record about work and daily life became training data for AI models.

The key on the frieze uses one term from improvisational theatre. When a later scene brings back something from an earlier one, improvisers call it reincorporation (Johnstone, 1979). The arcs on the timeline mark returns of this kind.

The timeline is a selection made for the frieze. Each work below is followed by its full citation. At the end are the art and design works, shown in ochre on the frieze, the other sources the frieze cites, and the models and data used to make it.

Machinic Vision: all films and pages

Neural networks

McCulloch & Pitts 1943

McCulloch & Pitts: neurons as logic

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

Hebb 1949

Hebb: The Organization of Behavior

Later taken up by Hopfield 1982.

Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. Wiley.

Minsky 1951

Minsky builds SNARC

Minsky, M. L. (1954). Theory of neural-analog reinforcement systems and its application to the brain-model problem [Doctoral dissertation, Princeton University]. SNARC was built with Dean Edmonds in 1951.

Rosenblatt 1958

Rosenblatt: the perceptron

Later taken up by Minsky & Papert 1969.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.

Widrow & Hoff 1960

Widrow & Hoff: the delta rule

Later taken up by Rumelhart, Hinton & Williams 1986.

Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. IRE WESCON Convention Record, Part 4, 96–104.

Linnainmaa 1970

Linnainmaa: reverse-mode differentiation

Linnainmaa, S. (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors [Master's thesis, University of Helsinki].

Griewank, A. (2012). Who invented the reverse mode of differentiation? Documenta Mathematica, Extra Volume ISMP, 389–400.

Anderson; Kohonen 1972

Anderson; Kohonen: associative memory

Later taken up by Hinton & Anderson 1981.

Anderson, J. A. (1972). A simple neural network generating an interactive memory. Mathematical Biosciences, 14(3–4), 197–220.

Kohonen, T. (1972). Correlation matrix memories. IEEE Transactions on Computers, C-21(4), 353–359.

Werbos 1974

Werbos: backpropagation (thesis)

Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences [Doctoral dissertation, Harvard University].

Fukushima 1980

Fukushima: the neocognitron

Later taken up by LeCun et al. 1989.

Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193–202.

Hinton & Anderson 1981

Hinton & Anderson: Parallel Models of Associative Memory

Hinton, G. E., & Anderson, J. A. (Eds.). (1981). Parallel models of associative memory. Erlbaum.

Hopfield 1982

Hopfield: networks with an energy

  • takes up and uses Hebb 1949 — Hebbian storage in a physical system

Later taken up by Ramsauer et al. 2020.

Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.

Ackley, Hinton & Sejnowski 1985

Ackley, Hinton & Sejnowski: Boltzmann machine

Later taken up by Hinton et al. 2006.

Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9(1), 147–169.

Rumelhart, Hinton & Williams 1986

Rumelhart, Hinton & Williams: backpropagation

Later taken up by Bengio et al. 2003.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.

LeCun et al. 1989

LeCun et al.: convolutional nets

Later taken up by AlexNet 2012.

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.

Hochreiter 1991

Hochreiter: vanishing gradients

Later taken up by Hochreiter & Schmidhuber 1997.

Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen [Diploma thesis, Technische Universität München].

Cortes & Vapnik 1995

Cortes & Vapnik: support vector machines

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Hochreiter & Schmidhuber 1997

Hochreiter & Schmidhuber: LSTM

Later taken up by Bahdanau et al. 2014.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

Bengio et al. 2003

Bengio et al.: neural language model

Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.

Hinton et al. 2006

Hinton, Osindero & Teh: deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.

ImageNet 2009

ImageNet; GPU training

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255).

Raina, R., Madhavan, A., & Ng, A. Y. (2009). Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th International Conference on Machine Learning (pp. 873–880).

AlexNet 2012

Krizhevsky, Sutskever & Hinton: AlexNet

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

Bahdanau et al. 2014

Sutskever et al.: seq2seq; Bahdanau et al.: attention

Later taken up by Vaswani et al. 2017.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27.

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.

Vaswani et al. 2017

Vaswani et al.: the Transformer

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

GPT-3 2020

Kaplan et al.; Brown et al.: scaling, GPT-3

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020). Scaling laws for neural language models. arXiv:2001.08361.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., . . . Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

Ramsauer et al. 2020

Ramsauer et al.: Hopfield networks is all you need

  • reads as the same as Hopfield 1982 — attention read as associative memory

Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2021). Hopfield networks is all you need. In International Conference on Learning Representations. arXiv:2008.02217.

CLIP 2021

Radford et al.: CLIP an ancestor of an instrument used to make the frieze

Later taken up by SAM; Grounding DINO 2023.

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (PMLR 139, pp. 8748–8763).

SAM; Grounding DINO 2023

Kirillov et al.: Segment Anything; Liu et al.: Grounding DINO an ancestor of an instrument used to make the frieze

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015–4026).

Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., Zhu, J., & Zhang, L. (2023). Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection. arXiv:2303.05499.

Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K. V., Carion, N., Wu, C.-Y., Girshick, R., Dollár, P., & Feichtenhofer, C. (2024). SAM 2: Segment anything in images and videos. arXiv:2408.00714.

Symbolic, search, evolution

Garfield 1955

Garfield: citation indexes for science

Later taken up by Brin & Page 1998.

Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.

Good 1965

Good: the intelligence explosion

Later taken up by Self-improvement 2026.

Good, I. J. (1965). Speculations concerning the first ultraintelligent machine. Advances in Computers, 6, 31–88.

Minsky & Papert 1969

Minsky & Papert: Perceptrons

Later taken up by Rumelhart, Hinton & Williams 1986.

Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press.

Spärck Jones 1972

Spärck Jones: term specificity (IDF) an ancestor of an instrument used to make the frieze

Later taken up by Salton et al. 1975.

Spärck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.

Salton et al. 1975

Salton, Wong & Yang: a vector space model

Later taken up by Lewis et al. 2020.

Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620.

MYCIN 1976

Expert systems (MYCIN)

Later taken up by Sutton 2019.

Shortliffe, E. H. (1976). Computer-based medical consultations: MYCIN. Elsevier.

Minsky 1986

Minsky: The Society of Mind

Later taken up by Zhuge et al. 2023.

Minsky, M. (1986). The society of mind. Simon & Schuster.

Pearl 1988

Pearl: Bayesian networks

Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.

Brin & Page 1998

Brin & Page: the anatomy of a web search engine

Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

AlphaGo 2016

Silver et al.: AlphaGo

Later taken up by o1; DeepSeek-R1 2024.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484–489. https://doi.org/10.1038/nature16961

Lewis et al. 2020

Lewis et al.: retrieval-augmented generation

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.

Chain of thought; ReAct 2022

Wei et al.: chain of thought; Yao et al.: ReAct

Later taken up by o1; DeepSeek-R1 2024.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35.

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations. arXiv:2210.03629.

Zhuge et al. 2023

Zhuge et al.: natural-language societies of mind

Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., Kirsch, L., Li, B., Li, G., Liu, S., Mai, J., Piękos, P., Ramesh, A., Schlag, I., Shi, W., . . . Schmidhuber, J. (2023). Mindstorms in natural language-based societies of mind. arXiv:2305.17066.

AlphaProof 2024

AlphaProof: proofs in Lean

Google DeepMind. (2024, July 25). AI achieves silver-medal standard solving International Mathematical Olympiad problems. https://deepmind.google/discover/blog/

Coding agents 2025

Coding agents

Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024). SWE-agent: Agent-computer interfaces enable automated software engineering. Advances in Neural Information Processing Systems, 37.

Coding agents in daily use from 2025, such as Claude Code (Anthropic) and Codex (OpenAI).

AlphaEvolve 2025

AlphaEvolve

Later taken up by Self-improvement 2026.

Novikov, A., Vũ, N., Eisenberger, M., Dupont, E., Huang, P.-S., Wagner, A. Z., Shirobokov, S., Kozlovskii, B., Ruiz, F. J. R., Mehrabian, A., Kumar, M. P., See, A., Chaudhuri, S., Holland, G., Davies, A., Nowozin, S., Kohli, P., & Balog, M. (2025). AlphaEvolve: A coding agent for scientific and algorithmic discovery. arXiv:2506.13131.

Self-improvement 2026

Recursive self-improvement as a goal

  • returns to, without yet acting on Good 1965 — the idea returns as a goal; no working mechanism yet
  • takes up and uses AlphaEvolve 2025

Duan, Y., Liu, Y., Tang, Z., Chen, H., Zhou, J., Liu, Y., Xu, B., Wu, Y., Chen, S., Zhou, Y., Wang, H., Yu, X., Han, S., Zhu, X., Zhou, Lu, B., Zhou, W., Liu, J., Fang, N., . . . Wu, F. (2026). The last AI built by humans: Toward genuine recursive self-improvement. arXiv:2609.11873.

Wu, S., Ren, J., Li, Y., Li, H.-S., Yang, C., Zhang, Y., Gu, W., Yang, J., Batista-Navarro, R., Zhang, C., Liu, X., Zhou, M., Dai, B., & Lin, C. (2026). ModularRSI: Modular and generalizable recursive harness self-improvement. arXiv:2609.14857.

Learning from reward

Thorndike 1911

Thorndike: the law of effect

Later taken up by Barto, Sutton & Anderson 1983.

Thorndike, E. L. (1911). Animal intelligence: Experimental studies. Macmillan.

Craik 1943

Craik: The Nature of Explanation

Later taken up by Ha & Schmidhuber 2018.

Craik, K. J. W. (1943). The nature of explanation. Cambridge University Press.

Samuel 1959

Samuel: checkers that learns

Later taken up by Sutton 1988.

Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229.

Rescorla & Wagner 1972

Rescorla & Wagner: prediction error

Later taken up by Sutton 1988.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). Appleton-Century-Crofts.

Barto, Sutton & Anderson 1983

Barto, Sutton & Anderson: adaptive elements

Later taken up by Christiano et al. 2017.

Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983). Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, 13(5), 834–846.

Holland 1986

Holland: escaping brittleness (classifier systems)

Later taken up by Sutton 1988.

Holland, J. H. (1986). Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2, pp. 593–623). Morgan Kaufmann.

Sutton 1988

Sutton: temporal-difference learning

Later taken up by Tesauro 1992.

Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3(1), 9–44.

Sutton 1990

Sutton: Dyna

Later taken up by Ha & Schmidhuber 2018.

Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the Seventh International Conference on Machine Learning (pp. 216–224). Morgan Kaufmann.

Tesauro 1992

Tesauro: TD-Gammon

Later taken up by Mnih et al. 2013.

Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3–4), 257–277.

Mnih et al. 2013

Mnih et al.: deep Q-networks

Later taken up by AlphaGo 2016.

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing Atari with deep reinforcement learning. arXiv:1312.5602.

Christiano et al. 2017

Christiano et al.: RL from human preferences

Later taken up by InstructGPT 2022.

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

Ha & Schmidhuber 2018

Ha & Schmidhuber: world models

Later taken up by World models 2025.

Ha, D., & Schmidhuber, J. (2018). World models. arXiv:1803.10122.

Sutton 2019

Sutton: The Bitter Lesson

Sutton, R. S. (2019, March 13). The bitter lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html

InstructGPT 2022

Ouyang et al.: InstructGPT; ChatGPT

Later taken up by Claude 2023.

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35.

OpenAI. (2022, November 30). Introducing ChatGPT. https://openai.com/index/chatgpt/

Claude 2023

Bai et al.: constitutional AI; Claude an ancestor of an instrument used to make the frieze

Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., . . . Kaplan, J. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv:2212.08073.

Anthropic. (2023, March 14). Introducing Claude. https://www.anthropic.com/news/introducing-claude

o1; DeepSeek-R1 2024

Reasoning models: o1; DeepSeek-R1

OpenAI. (2024, September 12). Learning to reason with LLMs. https://openai.com/index/learning-to-reason-with-llms/

DeepSeek-AI. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv:2501.12948.

World models 2025

World models now (Genie 3; programs as world models)

Google DeepMind. (2025, August 5). Genie 3: A new frontier for world models. https://deepmind.google/discover/blog/

Safron, A., Levin, M., Klimaj, V., Sheikhbahaee, Z., Sakthivadivel, D., Razi, A., Ha, D., Hay, N., Schmidt, K., Rish, I., Krakauer, D., Mitchell, M., Gershman, S. J., & Tenenbaum, J. B. (2026). World models, artificial general intelligence and the hard problems of life–mind continuity: Toward a unified understanding of natural and artificial intelligence. Philosophical Transactions of the Royal Society A, 384(2320), 20240533. https://doi.org/10.1098/rsta.2024.0533

Tsividis, P., Loula, J., Burga, J., Rodriguez, J. P., Arnaud, S., Foss, N., Campero, A., Subramanian, A., Pouncy, T., Gershman, S. J., & Tenenbaum, J. B. (2026). Human-level learning of complex novel tasks as theory-based modelling, exploration and planning. Philosophical Transactions of the Royal Society A, 384(2320), 20240529. https://doi.org/10.1098/rsta.2024.0529

Organizations and systems

Trist & Bamforth 1951

Trist & Bamforth: the longwall method of coal-getting

Later taken up by Bostrom & Heinen 1977, Sarker et al. 2019.

Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38.

March & Simon 1958

March & Simon: Organizations

Later taken up by Cyert & March 1963.

March, J. G., & Simon, H. A. (1958). Organizations. Wiley.

Cyert & March 1963

Cyert & March: A Behavioral Theory of the Firm

Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Prentice-Hall.

Bostrom & Heinen 1977

Bostrom & Heinen: MIS problems and failures, a socio-technical perspective

Later taken up by Sarker et al. 2019.

Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), 17–32.

Santa Fe Institute 1984

The Santa Fe Institute is founded

Later taken up by Holland 1995.

Santa Fe Institute (founded 1984), Santa Fe, New Mexico. https://www.santafe.edu

Zuboff 1988

Zuboff: In the Age of the Smart Machine

Later taken up by Zuboff 2019.

Zuboff, S. (1988). In the age of the smart machine: The future of work and power. Basic Books.

March 1991

March: exploration and exploitation in organizational learning

  • takes up and uses Holland 1975 — exploration and exploitation, after Holland; to check

Later taken up by Levinthal 1997.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

Kauffman 1993

Kauffman: The Origins of Order

Later taken up by Levinthal 1997.

Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. Oxford University Press.

Holland 1995

Holland: Hidden Order

Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Addison-Wesley.

Star & Ruhleder 1996

Star & Ruhleder: steps toward an ecology of infrastructure

Later taken up by Hanseth & Lyytinen 2010, Tilson et al. 2010.

Star, S. L., & Ruhleder, K. (1996). Steps toward an ecology of infrastructure: Design and access for large information spaces. Information Systems Research, 7(1), 111–134.

Levinthal 1997

Levinthal: adaptation on rugged landscapes

Levinthal, D. A. (1997). Adaptation on rugged landscapes. Management Science, 43(7), 934–950.

Orlikowski & Iacono 2001

Orlikowski & Iacono: desperately seeking the IT in IT research

Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134.

Hevner et al. 2004

Hevner, March, Park & Ram: design science in IS research

Later taken up by Abbasi et al. 2024.

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.

Mechanical Turk 2005

Amazon Mechanical Turk opens

Later taken up by Gray & Suri 2019.

Amazon Mechanical Turk (opened to the public 2005). https://www.mturk.com

Tiwana et al. 2010

Tiwana, Konsynski & Bush: platform evolution

Later taken up by de Reuver et al. 2018.

Tiwana, A., Konsynski, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675–687.

Hanseth & Lyytinen 2010

Hanseth & Lyytinen: design theory for dynamic complexity in information infrastructures

Hanseth, O., & Lyytinen, K. (2010). Design theory for dynamic complexity in information infrastructures: The case of building internet. Journal of Information Technology, 25(1), 1–19.

Tilson et al. 2010

Tilson, Lyytinen & Sørensen: digital infrastructures

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Digital infrastructures: The missing IS research agenda. Information Systems Research, 21(4), 748–759.

Watson et al. 2010

Watson, Boudreau & Chen: energy informatics

Watson, R. T., Boudreau, M.-C., & Chen, A. J. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38.

Mayer-Schönberger & Cukier 2013

Mayer-Schönberger & Cukier: Big Data (datafication)

Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

Helmond 2015

Helmond: the platformization of the web

Later taken up by de Reuver et al. 2018.

Helmond, A. (2015). The platformization of the web: Making web data platform ready. Social Media + Society, 1(2).

Faraj et al. 2018

Faraj, Pachidi & Sayegh: working and organizing in the age of the learning algorithm

Later taken up by Kellogg et al. 2020.

Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.

de Reuver et al. 2018

de Reuver, Sørensen & Basole: the digital platform

de Reuver, M., Sørensen, C., & Basole, R. C. (2018). The digital platform: A research agenda. Journal of Information Technology, 33(2), 124–135.

Zuboff 2019

Zuboff: The Age of Surveillance Capitalism

  • takes up and uses Zuboff 1988 — informate becomes behavioural surplus

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

Gray & Suri 2019

Gray & Suri: Ghost Work

Gray, M. L., & Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Houghton Mifflin Harcourt.

Sarker et al. 2019

Sarker, Chatterjee, Xiao & Elbanna: the sociotechnical axis of cohesion

Sarker, S., Chatterjee, S., Xiao, X., & Elbanna, A. (2019). The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly, 43(3), 695–719.

Kellogg et al. 2020

Kellogg, Valentine & Christin: algorithms at work

Later taken up by Möhlmann et al. 2021.

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.

Möhlmann et al. 2021

Möhlmann, Zalmanson, Henfridsson & Gregory: algorithmic management

Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4), 1999–2022.

Abbasi et al. 2024

Abbasi, Parsons, Pant, Sheng & Sarker: pathways for design research on AI

Abbasi, A., Parsons, J., Pant, G., Sheng, O. R. L., & Sarker, S. (2024). Pathways for design research on artificial intelligence. Information Systems Research, 35(2), 441–459.

Personal notes

Lyytinen, Nickerson & King (2021), “Metahuman systems = humans + machines that learn,” Journal of Information Technology: where my own work joins this line.

Art and design

These works run along the timeline in ochre. A dotted line ties each to the station of the frieze that takes it up.

  • Lönberg-Holm, K., & Sutnar, L. (1944). Catalog design. Sweet's Catalog Service.
  • Burtin, W. (art director, from 1948). Scope [magazine]. The Upjohn Company.
  • Bayer, H. (1953). World geo-graphic atlas: A composite of man's environment. Container Corporation of America.
  • Becher, B., & Becher, H. (1970). Anonyme Skulpturen: Eine Typologie technischer Bauten. Art-Press Verlag.
  • Weingart, W. (2000). My way to typography. Lars Müller Publishers. (The layered type of the early 1970s at the Basel school.)
  • Friedlander, L. (1976). The American monument. The Eakins Press Foundation.
  • Greiman, A. (1986). Does it make sense? Design Quarterly, 133. Walker Art Center and MIT Press.
  • Struth, T. (1987). Unbewusste Orte / Unconscious places [Exhibition catalogue]. Kunsthalle Bern.
  • Lombardi, M. (1994–2000). Narrative structures [Drawings]. See Hobbs, R. (2003). Mark Lombardi: Global networks. Independent Curators International.
  • Tillmans, W. (2000). Installation for the Turner Prize exhibition [Installation]. Tate Britain, London.

Other sources the frieze cites

  • Avital, M., Mathiassen, L., & Schultze, U. (2017). Alternative genres in information systems research. European Journal of Information Systems, 26(3), 240–247.
  • Crawford, K., & Joler, V. (2018). Anatomy of an AI system. AI Now Institute and Share Lab.
  • Farocki, H. (2003). Eye/Machine III [Video installation].
  • Johnstone, K. (1979). Impro: Improvisation and the theatre. Faber and Faber.
  • Lyytinen, K., Nickerson, J. V., & King, J. L. (2021). Metahuman systems = humans + machines that learn. Journal of Information Technology, 36(4), 427–445.
  • Paglen, T. (2016, December 8). Invisible images (your pictures are looking at you). The New Inquiry.
  • Seidel, S., Berente, N., Lindberg, A., Lyytinen, K., & Nickerson, J. V. (2019). Autonomous tools and design: A triple-loop approach to human-machine learning. Communications of the ACM, 62(1), 50–57.

How the frieze was made

  • Parts: Grounding DINO (Liu et al., 2023) finds the equipment named in each photograph, and SAM 2.1 (Ravi et al., 2024) cuts it out.
  • Phrases: the most distinctive terms (TF-IDF; Spärck Jones, 1972) of five years (2021–2026) of IS journal abstracts, arXiv abstracts in cs.AI and cs.CY, and Hacker News titles, each placed near the photograph that CLIP ViT-L/14 (Radford et al., 2021) matches it to.
  • Diagrams and type: TikZ and XeLaTeX, set in Helvetica Neue.
  • The crystal of agents: a diamond lattice grown by a simple rule and rendered in Blender (Cycles).

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