Bounded rationality: concepts

Herbert Simon, one of the original founders of AI as a discipline, the 1975 Turing Award winner and 1978 economics Nobel laureate, fundamentally challenged a pivotal assumption in neo-classical economic theory with his notion of ‘bounded rationality.’

Neo-classical economic theory posits that human agents are ‘perfectly’ rational, capable of accessing all necessary information, evaluating every potential option, and consistently making decisions that maximise their known utility functions. Simon (Reference Simon1955), however, introduced a nuanced alternative view; arguing that human decision-making is inherently limited by cognitive constraints and the finite amount of information, and time available, to make the decision. Simon argued that humans are not able to find the ‘optimal solution’ to every problem, because they have limited information, limited cognitive abilities, and limited time. Therefore, humans use heuristics, or rules of thumb, to simplify the problem and find a satisfactory solution, rather than an optimal solution.

Conlisk (Reference Conlisk1996: 686) has summarised the notion of bounded rationality as follows: ‘Human cognition is a scarce resource, implying that deliberation about economic decisions is a costly activity’. He makes compelling arguments as to why bounded rationality should be included in economic models and methodology more generally. Unfortunately, however, there are differences of opinion amongst economists as to what bounded rationality is, and how to best incorporate it into economic thinking.

Gerd Gigerenzer (Reference Gigerenzer and Viale2017) – a psychologist who has studied the use of bounded rationality and heuristics in decision making – has argued that there are three different understandings of the term ‘bounded rationality’ in the economics literature. The first understanding is that as first proposed by Herbert Simon. He studied how people make rational decisions in real-world situations of uncertainty, as opposed to well-defined situations of risk (see Knight, Reference Knight1921 for the seminal discussion of the differences between uncertainty and risk). Simon encouraged economists to study actual decisions under uncertainty rather than constructing as if models of expected utility maximisation.

The second understanding can be described as being ‘optimisation under constraint’. According to Gigerenzer this is how (some) economists adopted the term ‘bounded rationality’ but then subverted it to mean precisely the opposite to what Simon had intended. This approach assumes that agents have perfect information and unlimited time, but face some constraints such as budget, technology, or cognitive limitations. The decision makers then optimise their utility or profit subject to those constraints. AI is very likely to resolve bounded rationality as defined here. Conlisk (Reference Conlisk1996: 683–686), however, considers and debunks eight separate arguments that are routinely proposed in support of treating bounded rationality as simply being just another constraint to be included in an optimisation model.

The third understanding relates to how proponents of behavioural economics have appropriated the term bounded rationality. Gigerenzer argues that behavioural economists changed the meaning of bounded rationality to imply violations of rational choice theory, which they then interpreted as irrationality. This approach focuses on identifying and correcting the systematic errors or biases that people make when they use simple heuristics or rules of thumb to make decisions. Here Gigerenzer is being critical of the 2002 economics Nobel laureate Daniel Kahneman, and Amos Tversky. Gigerenzer (Reference Gigerenzer2015) is critical of the notion of libertarian paternalism. Similarly, Berg and Davidson (Reference Berg and Davidson2017) argue that libertarian paternalism suffers from a knowledge problem that makes it impossible for paternalists to second-guess consumer presences.

In theory, AI could work well to overcome human biases by providing objective analysis and decision-making based on data, rather than on human intuition, or preconceptions. In practice, however, AI has not always been particularly successful in this area (Manyika et al., Reference Manyika, Silberg and Presten2019). For example, in 2018, it was reported that an AI tool used by Amazon for recruitment had taught itself to favour male candidates over female candidates for technical jobs because it was trained on resumes submitted to the company over a 10-year period, which were predominantly from men. In 2016, it was reported that an AI tool was more likely to identify African Americans as being higher risk of recidivism than they were.

Gigerenzer's own research follows Simon's original definition of bounded rationality and is based on the idea that humans use simple heuristics, or rules of thumb, that are adapted to the structure of their environment and enable them to make ‘fast and frugal’ decisions under uncertainty, and time pressure. Heuristics, according to Gigerenzer, are not inferior or error-prone shortcuts, but rather smart and effective strategies that make best use of the limited information and computational capacity available to the decision maker. Gigerenzer argues that these heuristics are not just about computational efficiency, but also about robustness and simplicity in uncertain environments (Gigerenzer and Gaissmaier, Reference Gigerenzer and Gaissmaier2011).

Importantly, for our purposes Gigerenzer sets out three theoretical guidelines for thinking about bounded rationality: Take uncertainty seriously. Take heuristics seriously. Take ‘ecological rationality’ seriously. This last point requires some additional explanation.

Gigerenzer et al. (Reference Gigerenzer and Todd1999: 5) defined ecological rationality as rationality that is consistent with reality. Later they provide a better definition (Reference Gigerenzer and Todd1999: 13), ‘A heuristic is ecologically rational to the degree that it is adapted to the structure of an environment …’. Vernon Smith (Reference Smith2008: 36), who shared the 2002 economics Nobel Prize with Daniel Kahneman, paraphrases the Gigerenzer et al. definition as follows:

Smith (Reference Smith2008: 2) had already explained:

Smith's argument here is Hayekian. Decisions are embedded within evolved institutions, consequently context, time, and place all matter when evaluating ‘rationality’. Mousavi (Reference Mousavi, Frantz, Chen, Dopfer, Heukelom and Mousavi2017: 91) has summarised the overlap in Gigerenzer's and Smith's understanding of ecological rationality and can be paraphrased as follows: Both use the same definition of ecological rationality; heuristics can be substituted by markets, firms, and rule systems; rules emerge from social behaviour and the choice of heuristic is not always deliberate; the evaluation of heuristics must be grounded in real-world experiences rather than theoretical sophistication. It is this last point that is important for our purposes – AI constitutes theoretical sophistication, but it is an open question as to how AI gains ‘real-world experience’.

Lynne Kiesling (Reference Kiesling, Boettke and Coyne2015: 53) describes the same phenomenon as the ‘contextual knowledge problem’:

Kiesling argues that this form of knowledge forms part of individual human perceptions and does not exist independently of a market context (see also Thomsen, Reference Thomsen1992 for an extended discussion on this point). Importantly, Kiesling (Reference Kiesling, Boettke and Coyne2015: 55) argues that this knowledge cannot be replicated by an ex-ante, non-market mechanism.

AI is an ex-ante, non-market mechanism.

The point here is that AI may have access to information (many data points) but cannot have access to knowledge. As Boettke (Reference Boettke2002: 266) explains, ‘information is the stock of the existing known, while knowledge is the flow of new and ever expanding areas of the known’. Furthermore, that new knowledge comes into existence ‘only because of the context in which actors find themselves acting’ (Boettke, Reference Boettke2002: 267). Information can be discovered through search functions and AI is very good at collating information. Knowledge, however, is discovered through purposeful human action. Knowledge requires interpretation and judgement, while information is basic facts (Klein, Reference Klein2012: 320).

The knowledge problem: Mises and Hayek

If solving the ‘economic problem’ were simply a matter of pure logic – as the neoclassical school suggests – then the advent of AI is hugely advantageous in that regard. An economic agent that is neither opportunistic, nor suffers from bounded rationality (as defined by neoclassical economists), would ensure that ‘technosocialism’ could work (See Boettke and Candela, Reference Boettke and Candela2022 for a discussion of technosocialism). As Ludwig von Mises (Reference Mises1920 [1990], Reference Mises1922 [1981], Reference Mises1944 [2007], Reference Mises1949 [1996]) and Friedrich Hayek (Reference Hayek and Hayek1935, Reference Hayek1940, Reference Hayek1945) argued, however, solving the economic problem is not one of pure logic (see Boettke, Reference Boettke2019 for a discussion of the similarity of Mises' and Hayek's views and analysis). As Gigerenzer has argued bounded rationality is not just another constraint that can be resolved via technology, nor is it irrationality.

In his 1922 analysis of socialism and central planning, Mises (Reference Mises1922 [1981]: 101) includes this passage:

Following on from the discussion of bounded rationality, it is clear that Mises has identified a similar cognitive problem. He also appears to argue that the division of labour – both physically and mentally – is a response to that condition. In his 1944 discussion of Bureaucracy, Mises restates that point slightly differently:

That last sentence could very easily be interpreted as suggesting that technology is the solution to the economic problem. Mises could not have imagined the advances in AI that have been achieved since he wrote those words. Yet – it is clear from his very next sentences that ‘technology’ is not the solution to the economic problem.

Solving the economic problem involves having humans make choices. Of course, there are many tools that can be used to inform decision making or make better decisions. For example, linear programming seems an obvious tool to answer some of the questions that Mises is asking (Dorfman et al. (Reference Dorfman, Samuelson and Solow1958). Then there are various decision-making rules that can be employed such as maximising expected value or the minimax regret rule, and so on. Similarly, there are processes (usually labelled ‘strategic planning’) that humans can employ to improve decision making. Over time various institutions have evolved to improve decision-making – here, for example, we see the separation of powers within political governance or the separation of decision management and decision control in corporate governance (Fama and Jensen, Reference Fama and Jensen1983).

It is true that humans can program machines to perform tasks as if they were making decisions. Consider the trivial case of a vending machine: in example for a payment the vending machine provides a good. The machine itself, however, has made no choice or decision – it has been programmed by a human to perform in a particular way. All choices and decisions within an economy are ultimately made by humans. This includes delegated decisions. One of the benefits of AI – and industrialisation generally – is that many tasks previously performed by humans are now being delegated to machines.

Delegation is common within organisations (see Foss and Klein, Reference Foss and Klein2012 for a discussion on delegated entrepreneurship). Delegating to a human agent, however, gives rise to the agency problem (Jensen and Meckling, Reference Jensen and Meckling1976). Delegating to a non-human agent, such as AI, however, should not give rise to an agency problem (see Allen et al., Reference Allen, Berg, Ilyushina and Potts2023). That insight, however, is trivial. Machines are simply not opportunistic (as defined by Williamson, Reference Williamson1975, Reference Williamson1985 – see below). The challenge in delegating to non-human agents is in communicating tacit knowledge (itself difficult with human agents), maladaptation (when real world conditions deviate substantially from expected conditions – see Aoki, Reference Aoki1983), and ensuring that the non-human agent performs as expected (the program is bug free). Delegating decision making to AI promises to economise on human cognition (see Berg et al., Reference Berg, Davidson and Potts2018). As Alfred Whitehead indicated, ‘Civilization advances by extending the number of important operations which we can perform without thinking about them’ (quoted in Hayek, Reference Hayek1945: 528). It is not immediately clear that the various costs (and benefits) of AI delegation have been fully evaluated. At the very least, agency costs (delegation to another human) need to be traded off against the costs of delegating to an AI – those costs would include (net) tacit information communication costs, (net) maladaptation costs, and coding error costs. A full discussion of these issues is well beyond the scope of this paper and is a topic for further research (see Berg et al., Reference Berg, Davidson and Potts2023 for preliminary research on these issues).

It has long been argued that computing technology can resolve the knowledge problem. Oskar Lange (Reference Lange and Feinstein1967), for example, had proposed a computing solution (see Lavoie, Reference Lavoie1985 [2016] generally, and specifically Davidson, Reference Davidson2023 for a discussion of the Chilean Cybersyn project that attempted to implement this very ‘solution’). Here we discuss the 1973 economics laureate Wassily Leontief's argument for ‘smart machines’ (quoted in Silk, Reference Silk1983). The late Don Lavoie – an economist who had an undergraduate qualification in computer science – responded to this argument as follows (Reference Lavoie1985 [2016]: 118–119):

While it is the case that LLMs do not need to be ‘explicitly told’ by their programmers, the general principle remains the same. AI – smart machines – only ‘know’ data that can be programmed into, or read, by a machine. Despite their ‘learning’ even LLMs, unlike humans, are not capable of ‘generalizing beyond their pretraining data’ (Yadlowsky et al., Reference Yadlowsky, Doshi and Tripuraneni2023: 2).

It is here that we introduce Hayek (Reference Hayek1945) into the analysis. He had argued (Reference Hayek1945: 519–520):

Hayek too could never have imagined the advances in AI technology that have occurred since he wrote those words. Yet if we examine the ‘data’ that are required to centrally plan, it quickly becomes clear that AI is not the solution to the economic problem either. Hayek (Reference Hayek1945: 519) had argued, ‘If we possess all the relevant information, if we can start out from a given system of preferences, and if we command complete knowledge of available means’ the economic problem can be solved by logic. Hayek was responding to a claim by Oskar Lange (Reference Lange1936) that three pieces of information were necessary to solve the economic problem: Prices, preferences, and resources. Lange had argued that central planners with their knowledge of preferences and resources could solve for prices in a centrally planned economy. Hayek's argument was that Lange had assumed the answer to the knowledge problem. Hayek's argument is that preferences and resources are discovered; they are not ‘given’. Kiesling (Reference Kiesling2023) explains this point with an excellent example of contextual knowledge.

It is true that AI (and digital technology generally) can be used for forecasting demand and inventory management and the like. Tools to perform these tasks already exist but it is likely that AI can drive costs savings and efficiencies in performing these business functions. It is easy to imagine that an AI that has access to localised weather information and past consumer behaviour might forecast that demand for cold drinks might be higher on some days and not others. Humans are able to make those judgements too.

It is here that Gerd Gigerenzer's notion of ecological rationality echoes Hayek's understanding of the economic problem. In Hayek's view, individuals, with their own limited and often disparate bits of knowledge, make decisions based on their local circumstances. This decentralised decision-making process reflects ecological rationality in that individuals use the knowledge and resources available in their environment to make decisions that are ‘good enough’ rather than seeking an optimal solution which may be practically impossible due to the dispersed nature of information.

Tacit knowledge, however, must be transformed (at some cost) into explicit knowledge and then communicated to the AI. Discovering resources that can be deployed for economic activity is an entrepreneurial insight. Israel Kirzner (Reference Kirzner1997: 34–35) describes entrepreneurial insight as follows:

Again, ecological rationality is important. Entrepreneurs make decision based on local knowledge and preferences in an environment where they believe that prices are ‘wrong’. Entrepreneurs need to understand the context in which they operate. This requires tacit knowledge, or knowledge gained from personal experience. AIs are trained on existing (given) information sets. They make probabilistic decisions i.e. they are able to make decisions involving risk, not uncertainty. In short, AIs are backward looking where entrepreneurs are forward looking.

Potts (Reference Potts2010, Reference Potts, Frantz, Chen, Dopfer, Heukelom and Mousavi2017) makes the argument that even for humans' entrepreneurial insight is difficult. He identifies ten separate behavioural characteristics that exacerbate what he calls ‘the problem of choice over novelty under uncertainty’. The very first characteristic is ‘awareness of novelty is hard’. Potts' argument is that in a world of rapid change that humans tend to overlook genuine novelty. If humans struggle to observe novelty under real-world conditions, it is unclear how AI that is entirely backward looking can observe novelty.

Phelan and Wenzel (Reference Phelan and Wenzel2023: 177–178) make a similar point. They argue that to solve the economic problem, ‘we would need an artificially intelligent entrepreneur’. They go on to explain that what would be required is, ‘a machine that can ‘think outside the box’’. The problem with that argument – although Phelan and Wenzel do not use this terminology – is that AI cannot ‘think’ outside the box; the AI is the box. Similarly, Lambert and Fegley (Reference Lambert and Fegley2023: 246) point out that all the data used to train AI, ‘are entirely that of the past’. AI that is trained on a sub-set of known information that can be easily communicated in machine readable form cannot ‘know’ enough to replicate the entrepreneurial function.

AI and organisation: Williamson and Rothbard

New institutional economics (NIE) is a branch of economics that studies how institutions shape human behaviour and economic outcomes. Institutions are the formal and informal rules, norms, and customs that govern social interactions, such as laws, contracts, property rights, markets, organisations, and culture. NIE assumes that individuals are rational and self-interested, but also bounded by cognitive limitations, incomplete information, and transaction costs. Therefore, institutions matter because they reduce uncertainty, facilitate cooperation, and coordinate expectations among economic agents. Economists who have made significant contributions in this field include Armen Alchian (Reference Alchian2006), Yoram Barzel (Reference Barzel1989, Reference Barzel2002), Ronald Coase (Reference Coase1937, Reference Coase1960), Harold Demsetz (Reference Demsetz1988), Douglass North (Reference North1990), Elinor Ostrom (Reference Ostrom1990), and Oliver Williamson (Reference Williamson1975, Reference Williamson1985) (see also Foss and Foss, Reference Foss and Foss2022: 37–62 for further discussion).

Oliver Williamson, the 2009 Nobel laureate in economics (joint with Elinor Ostrom), argued that bounded rationality affects the choice of governance structures, because it exacerbates uncertainty and complexity in transactions. For example, bounded rationality leads to incomplete contracts that cannot specify all possible contingencies. Bounded rationality may also limit the ability of parties to monitor and enforce contracts.

According to Williamson (Reference Williamson1985), bounded rationality is one of the two main behavioural assumptions of Transaction Costs Economics (a sub-field within NIE); opportunism is the other behavioural assumption. Opportunism is the tendency of some actors to not only act in their own self-interest but pursue their self-interest with guile. Williamson claimed that opportunism is a realistic assumption that reflects human nature and cannot be ignored in economic analysis. He also suggested that opportunism is exacerbated by bounded rationality, because it contributes to information asymmetry, leading to adverse selection and moral hazard.

Bounded rationality is important to Oliver Williamson's research agenda because it explains why different governance structures emerge and how they affect economic performance. Governance – how Williamson describes ‘hierarchy’ – emergences when bounded rationality, opportunism, and asset specificity are present. In the absence of asset specificity, we see what Williamson labels ‘competition’ i.e. market transactions. In Williamson's schema, the absence of bounded rationality, or unbounded rationality as he labels it – gives rise to ‘Planning’. Planning is an efficient contracting process in the context of unbounded rationality.

Again, this invites speculation that AI can reduce bounded rationality problems and can facilitate planning. It is here that the disconnect between central planning at a national level and planning at a corporate level becomes important. Large corporations appear to be very successful at planning – yet central planning at a national level is unsuccessful. Ronald Coase had asked the question, ‘Why is not all production carried on in one big firm?’. Some multinational corporations are ‘larger’ than some nation-states – how is it that these corporations appear to plan successfully, but those smaller nation-states cannot? What are the limits to corporate planning and does AI change those limits?

Klein (Reference Klein1996: 7) argues that several answers to Coase's question have been suggested, including some by economics laureates:

Klein suggests that Rothbard (Reference Rothbard1962 [1970]: 544–560) has a better solution to Coase's question that involves extending Mises' economic calculation problem to the firm. Rothbard makes the argument that vertically integrated firms are only able to establish profitability by reference to prices that are determined in external markets – ‘there can be no implicit estimates without an explicit market!’ (Reference Rothbard1962 [1970]: 543, emphasis original). In Rothbard's framework, this creates an upper bound on the size of the firm – for every capital good (stage of production in Rothbard's explanation) there must be a market where that good is traded and a market price established (Reference Rothbard1962 [1970]: 548):

Here Rothbard has not only reconciled Mises' and Hayek's views with those of Coase, but he has also provided an additional insight. Planning fails when it is centralised. Successful planning is local planning that takes into account local knowledge. In his recent history of the modern corporation over the twentieth century, Richard Langlois (Reference Langlois2023: 152) provides (anecdotal) evidence to support Rothbard's argument.

General Motors had attempted to centralise decision-making and codify local information to facilitate that centralisation. Yet without external market knowledge it was unable to manage its internal operations and planning efforts. Langlois (Reference Langlois2023: 151) summarises this insight as follows: ‘Federal decentralization – the M-form – works well when it is possible to identify business units that produce a distinct product for a distinct market.’ That is Rothbard's argument too. External market information is necessary to facilitate internal planning.

Williamson (Reference Williamson1985: 52) too is hostile to the notion of central planning, but not just on the grounds of bounded rationality.

His major argument against central planning (note that central planning is not his research interest) is that central planners are opportunistic. This differs from Mises and Hayek who were abstracting from the ‘incentive problem’. Williamson's argument does not explicitly rule out a technology that reduces bounded rationality making central planning (more) viable. It is important to note, however, that Williamson's arguments do not exclude strategic planning within organisations. What is particularly important for our purposes is that Williamson explains the emergence of the M-form organisation as a form of decentralisation of decision making.

In his 1975 treatment of markets and hierarchies, Williamson (Reference Williamson1975: 4–5) carefully distinguishes his position from Hayek's position on central planning. After summarising Hayek's (Reference Hayek1945) analysis, Williamson observes (emphasis original):

It appears that Williamson's (Reference Williamson1975) rejection of the possibility of central planning is even stronger, indeed more emphatic, than that of Mises and Hayek. Mises (Reference Mises1920 [1990], Reference Mises1922 [1981]) had argued that in the absence of money prices that economic calculation was impossible. Even in the presence of money prices, Williamson argues that central planning will fail. That insight informs his interpretation of the emergence of the M-form organisation. Williamson bases his argument on Chandler's (Reference Chandler1962, Reference Chandler1977) discussion of the emergence of the M-form at General Motors. Langlois (Reference Langlois2023), building on Freeland (Reference Freeland2001), suggests that the actual functioning on the M-form organisation at GM was somewhat different to how it has been described. It is not immediately clear, however, that the historical discrepancy is (sufficiently) inconsistent with Williamson's theory.

Williamson discusses bounded rationality in far more detail in his 1975 book than he does in 1985. His 1985 treatment seems perfunctory. Using Gigerenzer's language it seems that Williamson (Reference Williamson1985) is taking uncertainty seriously, but it is not clear that he is taking heuristics seriously (see Foss, Reference Foss2001, Reference Foss and Rizzello2003), nor seemingly does he incorporate ecological rationality into his analysis. His 1975 treatment of bounded rationality, however, is far more complete.

Williamson (Reference Williamson1975: 21–23) contains his most comprehensive discussion on bounded rationality. There he explains that bounded rationality includes both ‘neurophysiological limits’ and ‘language limits’. The neurophysiological limits relate to limitations on individuals' ability to ‘receive, store, retrieve, and process information without error’ (Reference Williamson1975: 21). Language limits are limitations on the ability of individuals to communicate with other individuals in a manner that fully describes their intent (Reference Williamson1975: 22). It seems plausible, however, that AI can relax a bounded rationality constraint if it is defined in these terms. AI can outperform human intelligence in storing, retrieving, and processing information. Similarly, AI's can communicate with other AI's in unambiguous terms.

It is here that Williamson (Reference Williamson1975: 22) discusses ecological rationality – while not actually using that terminology.

Surprisingly, Williamson does not differentiate between risk and uncertainty is a Knightian sense. He does, however, argue that uncertainty and complexity is exacerbated by ‘information impactedness’. This is not just a situation of asymmetric information, but an information problem that cannot be costlessly resolved (if at all). In his later work (Reference Williamson1985: 21) he also refers to ‘maladaptation costs’ as being a source of uncertainty. This is the cost of having to renegotiate contracts after changing circumstances have altered the intended outcome of the initial contract.

Williamson describes the emergence of the M-form organisation in similar terms to Rothbard's explanation of why a single firm does not dominate the entire economy. Profit centres are established where profitability can be observed (presumably with reference to external markets) and operational decisions are made at lower levels of the hierarchy while strategic decisions are made at higher levels of the hierarchy. As Freeland (Reference Freeland2001) and Langlois (Reference Langlois2023) have argued, historically there was not a clear-cut differentiation between strategic and operational decision making. As Langlois (Reference Langlois2023: 153) explains:

What Langlois is describing here is a discovery process where local knowledge is transformed into general knowledge (at least within the organisation). This is not a central planning process where local information is codified and transmitted to the centre. Rather this is a discovery process where in the face of actual decisions being made, decision makers must ‘actively interpret the information [they] receive, and pass judgement on its reliability and its relevance for … decision-making’ (Boettke, Reference Boettke2002: 267). This is a process where information is transformed into the knowledge necessary to make decisions.

These issues that Williamson identifies are unlikely to be resolved by AI. How AI ‘receives’ information is important to resolving bounded rationality problems. Once information is received AI can outperform a human at many margins. Communicating that information in a digitally readable format, however, remains a challenge and AI faces a ‘language’ challenge (albeit of a different character) as do humans.

The fact remains that AI can overcome many computational limitations that humans face and as such relax some of the bounded rationality constraints within Williamson's framework. The language problem of communicating information to the AI, however, remains. AI does not fully eliminate bounded rationality. It does, however, expand the range of planning that can occur. In the Williamson framework, that observation leads to the prediction that we should observe less hierarchy and more planning because of AI. That observation, however, does not necessarily imply better planning.

It can be argued that the increase in planning could circumvent the bounded rationality problem. Williamson is ultimately concerned with the problem of incomplete contracts. AI could provide a ‘brute force solution’ to incomplete contracting by simply simulating all (or at least, many) possible future scenarios and then developing a continency plan for each situation. Kiesling (Reference Kiesling2023), for example, speculates as to this very situation:

As she indicates this ‘solution’ does not resolve the contextual knowledge problem, but it could resolve the incomplete contracting problem. As indicated above, however, at present AI is not capable of making accurate out-of-sample predictions (Yadlowsky et al., Reference Yadlowsky, Doshi and Tripuraneni2023). That capability may improve in future but that may fall into Minsky's argument that ‘easy things are hard’ – humans can easily imagine the future.

In summary, AI relaxes some of the constraints that bounded rationality imposes on economic activity that then gives rise organisational forms such as corporate hierarchy. It does not, however, overcome problems of information impactedness nor maladaptation of contracts to changing circumstances. AI should result in less hierarchical organisational forms, but it is not clear if that implies fewer hierarchies or smaller hierarchies.

What sort of technology is AI?

AI is best understood as a new general-purpose technology (GPT) that has ubiquitous application and transformative potential across different sectors of the economy. Like the steam engine, electricity, computers, or the internet, which have greatly transformed both the economy and society at large, AI is not bound to a single specific application but is foundational, opening up wide arrays of uses. By automating tasks that required human cognition, from basic data processing to complex problem solving, it effectively augments human capital. This augmentation of human capital has the potential to dramatically increase productivity, mirroring the impact of previous GPTs like electricity, which extended the working day, or the Internet, which revolutionised communications and information access. Moreover, AI facilitates coordination across different areas of human activity by enabling efficient data processing, prediction, and decision-making, thereby reducing the cognitive load on individuals, and enhancing productivity in a broad array of tasks.

While AI can optimise existing institutional mechanisms that economise on bounded rationality, providing more efficient tools for coordination and decision-making, it is less likely to induce the emergence of entirely new institutional structures. AI can simplify complexity but does not resolve contextual knowledge problems. Human insight and decision-making remain necessary to resolve the economic problem at both the macro and micro levels of the economy.