Addendum: Evolutionary thinking in current neuroeconomics

Before I deal with the question of how to connect internal and external evolutionary mechanisms, I want to show that evolutionary thinking is already emerging in current neuroeconomics. This relates to the strand of research that builds on neural networks to explain choice. The standard approach in neuroeconomics starts out from the idea of a modular brain and looks for localized mechanisms in which choice is realized in a serial way. The exemplary and seminal research along this paradigm was done by Padoa-Schioppa and collaborators and has been condensed in programmatic reviews (Padoa-Schioppa 2011). A core idea is that subjective value is embodied in certain brain regions (the orbitofrontal cortex) and that choice is governed ‘downstream’ by these neurons, such that, for example, there are no sensorimotor feedback circuits influencing subjective value. This would clearly distinguish ‘economic values’ from other forms of valuation in the brain and would thus be essential for establishing ‘neuroECONOMICS’ as a separate field of research. At the same time, this model would be congruent with the basic economic model of choice (which includes aspects such as transitivity). Consequently, Padoa-Schioppa christened the model as ‘goods based’: That means, the alternatives of choice are interpreted as ‘goods’ in the economic sense, and choice is based on an abstract and subjective representation of value aka ‘utility’.

Against this view, Hunt and Hayden (2017) have presented a model of distributed and hierarchical neural networks. This view goes back to Hebbian connectionism and therefore would be directly comparable to a Hayekian approach to neuroeconomics. The fundamental difference is that subjective value would be conceived as an emergent property of network dynamics which is grounded in regular neuronal activity of mutual inhibition that implies the recurrent comparison of activity levels in hierarchically structured networks in which those patterns of mutual inhibition would be further processed. This was exactly Hayek’s starting point. These comparisons happen across all brain areas, so that local specialization combines with cross-area connectivity. That reflects that choices are always multidimensional (such as involving taste, smell and colour of food). In addition, these networks are recurrent (a central point made by Edelman), such that sequences of neuronal activity are always fed back to the current sequence, in this sense involving processes of short-term memory. This catches the important property of real-world choices which constantly need to revaluate ongoing behaviour (for example, when to stop eating a meal). Against the sequential view, the notion of multiple time scales is added: Patterns of mutual inhibition, mapping and re-entry emerge with different time scales, which allows for complex comparisons of valuation that match with the needs of behavioural regulation. In sum, the model is following the general connectionist paradigm as developed by leading neurophilosophers and is eliminative with reference to notions such as subjective value.

This view does not preclude that at certain stages of the process, some areas or even single neurons would obtain a central, though transient function in further channelling the network dynamics. This points to the basic difficulty in neuroscience how to infer causality from certain interventions such as lesions. But the fundamental paradigm would be clearly different from the established one in neuroeconomics. This is recognized by Padoa-Schioppa. In the review Padoa-Schioppa and Konen (2017), the evolutionary approach is discussed under the heading of ‘distributed consensus model’. They reject the model mainly based on two observations, i.e. the evidence on modularization and the evidence regarding low or lack of motor feedback circuits. However, at the same time Padoa-Schioppa has turned to network models as theoretical grounding of his ‘goods-based model’. Rusticchini and Padoa-Schioppa (2015) build on a purely connectionist network model of perceptual decisions by Wang. This network endogenously reproduces the differentiation of neuron activities in different types, including subjective value representation. In fact, Hunt and Hayden cite this work as supporting their own alternative model!

How can we reconcile these different interpretations of connectionist, i.e. ‘Hayekian’ models of the brain in economics and neuroeconomics? I suggest the following. In Padoa-Schioppa (2011) the basic model grounds in the notion of ‘integration’ of multiple dimensions of choice into subjective value. One central argument in the standard model is that subjective value cannot be stored in memory because otherwise choice could not adapt flexibly to situational context. That implies, however, that integration must happen at very high speeds. If we approach this integration as an evolutionary process, we can still assume that ultimately the results may be represented in special, but transient and intermediary neuronal structures of the OFC that enable choice among the alternatives. In other words, the goods-based model would be reduced to a specific form and stage of algorithmic implementation of the more basal evolutionary process.

That being said, the standard model only analyses choices between clearly defined alternatives. Although this is indeed what economics also does in its standard model, many choices that are ‘economic’ do not fit that pattern, even though alternatives are considered. But choosing among different quantities of juice or clearly defined lotteries is very different from taking an entrepreneurial decision under uncertainty. I think if we widen the scope of economic decisions conceptually, the evolutionary approach becomes even more plausible.


Hunt, Laurence T. and Benjamin Y. Hayden (2017): A Distributed, Hierarchical and Recurrent Framework for Reward-Based Choice. Nature Reviews Neuroscience 18(3) 172–82.

Padoa-Schioppa, Camillo (2011): Neurobiology of Economic Choice: A Good-Based Model. Annual Review of Neuroscience 34(1): 333–59.

Padoa-Schioppa, Camillo and Katherine E. Conen (2017): Orbitofrontal Cortex: A Neural Circuit for Economic Decisions. Neuron 968(4): 736–54.

Rustichini, Aldo and Camillo Padoa-Schioppa (2015): A Neuro-Computational Model of Economic Decisions. Journal of Neurophysiology 114(3): 1382–98.


Evolutionary mechanisms

To my best knowledge, neuroeconomists never refer to Friedrich Hayek as founder of their discipline. But he should be included at least in the list of the most important intellectual progenitors. His 1952 book ‘The Sensory Order’ is a highly abstract philosophy of the brain, came surprisingly close to Hebb’s emerging paradigm of connectionism, and it anticipated Gerald Edelman’s theory of ‘neuronal group selection’, as Edelman himself later recognized. The book does not deal with economics, but Hayek always asserted that it laid the ground for his epistemology and methodology of economics.

The central idea of the book is to conceive the brain as an evolutionary and, in some modern terminology, ‘autopoetic’ system. This points to a possible, albeit fundamental misunderstanding of the brain in most mechanistic reconstructions of the neurosciences. Mechanistic models assume that there are chains of cause and effect that work together in producing an outcome. Hayek’s and Edelman’s theory suggest a very different picture: Cause and outcome are connected via complex evolutionary mechanisms. What does that mean?

It means that once a sensory input and trigger for behaviour arrives, the brain would generate a wide range of alternative trajectories producing various outcomes simultaneously. These trajectories would compete against each other, until one would prevail, and effectively generates behaviour. The mechanistic misunderstanding of this happens because ex post only the ‘survivor’ is visible, and all alternative trajectories are lost. This is a ‘winner takes all’ scenario which seems very wasteful. Why would such a system evolve by natural selection?

I think that this connects with the extreme complexity of human social interaction. Even in simplest situations, misunderstanding is possible, and often minor causes might escalate to serious conflicts. That means, adaptive behaviour would need to rely on very fast feedback circuits in order to finetune outcomes and avoid mis-coordination. This cannot be achieved by a system that would operate sequentially, i.e. first launch one trajectory, then correct errors, and launch another trajectory. Waste is efficient here: The brain launches many alternatives simultaneously, and via feedbacks ultimately one is selected.

The point is that evolutionary mechanisms allow for the simultaneous choice of means and goals, whereas the standard neuroeconomic model takes goals as a given. In even simple situations of social interactions, goals can be multiple and opaque. This is important when analysing addiction, for example. Drinking a whiskey at the bar together with others can have many functions at the same time, not just taking up a certain does of alcohol, but, for instance, signalling friendship or lowering social distance. The point is that which goal dominates cannot be planned in advance, so ‘choice’ is not just choosing the means, but also the goal.

In my view, many issues in neuroscience and neuroeconomics can be approached in a new and productive way. Just a few examples. First, attention is a central aspect of human behaviour: In the evolutionary view, attention creates the specific niche in which the evolutionary mechanism unfolds. Second, there are many phenomena of relapse and rapid recovery of apparently ‘unlearned’ behaviour, but also sudden and unpredicted switches: If we assume that always many trajectories are present simultaneously, this is straightforward to explain, because evolutionary processes have a complex-dynamics with tipping points, multiple equilibria and more. Third, there is the intriguing discovery of the default mode system which has the surprising property of reduced activity once action sets in: In the evolutionary view that is to be expected because once one trajectory materializes, all others close. Fourth, human individuals have the property of being creative and they feel free, which is often seen in tension with neurophysiological causal determinism: In the evolutionary perspective, this freedom reflects the wide range of possible trajectories that might be triggered by external inputs.

To sum up, mechanistic philosophy of the neurosciences and neuroeconomics should go back to Hayek’s ideas and establish an evolutionary framework for causal analysis. As I will argue in the next post, this also means, again following Hayek, to connect evolutionary mechanisms in the brain with evolutionary mechanisms in the social world.


(This post builds on my chapter in the INSOSCI volume that we currently prepare for publication.)


The Case for an Aristotelian Oath in Finance

In the recently published ‘Festschrift’ for Karl-Heinz Brodbeck I have a paper (in German) that argues in favour of an ‘Aristotelian Oath’ in finance. This goes back to the insights gained by our INSOSCI project. The core idea is that agency in the financial sector manifests a collectively shared identity. Hence, resolving certain problems of regulating the sector must also rely on changing these identities. Why an oath? Why ‘Aristotelian’?

After the financial crisis, a movement was launched at some leading business schools in the US that propagated the idea of an oath, combined with certain ritual practices. That movement faded away but seems to experience a revival recently. The model would be the Hippocrates oath in medicine. Though not practiced today, it still plays a strong role in conceptions of ethics in medicine. The core point is that the oath solemnly declares various values and commitments that define the professional identity of actors. My use of ‘Aristotelian’ relates to Aristotle’s notion of money and ‘chrematistics’, arguing that money is a unique economic good since the desire for money is never satiated, which creates serious ethical issues. Therefore, I suggest an ‘Aristotelian oath’ for finance.

Economists typically reject the idea that ethics can be stronger than economic incentives. But that is exactly Aristotle’s point: The desire for money is extremely strong and would overcome all other motives, with harmful consequences. Indeed, it is straightforward to diagnose ethical failure in finance: Just read what a leading economist and then President of the American Finance Association, Luigi Zingales, had to say on that in his ‘Presidential Address’. Fraud, deception, misinformation, corruption, criminal bending of rules, all that what has been dubbed ‘Phishing for Phools’ by Nobel laureates Akerlof and Shiller. There is even experimental evidence of a team including Ernst Fehr that assuming the banker’s identity significantly enhances the willingness to cheat. As a result, according to opinion surveys, the bankers’ profession is one among the lowest reputation in the public. Bankers should worry about that, and if they don’t, it seems proof of ethical failure, indeed.

The solution normally offered by economists is regulation. But I think that this is wrong, or at least, cannot be the only solution. The reason is that money is such a strong incentive, as recognized by Aristotle. And there is a catch. Regulation always constrains the actions of actors, which means that only few of them will be able to go beyond those constraints. But then the profit opportunities are especially strong! Those actors who find ways to circumvent the regulation will always gain much higher profits than others. That can be done both by legal and illegal means, with a wide grey area in between, as we saw in the financial crisis. That means, all regulation will always create the incentives to erode it, in the longer run, thus triggering cycles of regulation, deregulation and reregulation, never really resolving the fundamental issues.

Therefore, I believe that regulation without changing the identity of actors is nothing but a theatre to make the public believe that politics is acting, after all. In which way? Well, similarly to the identity of doctors as embraced in the Hippocrates oath. Finance professionals should perceive themselves as providing a public service, strongly bound by commitments to the public interest, not only vis-à-vis their clients, but also to society at large. They should express commitment to highest standards of quality, transparency and customer orientation in designing and trading financial products. Their professional goal would not be to maximize profits, neither their own nor of a limited group of principals, but to improve the services, broaden inclusion and strengthening the “health”, almost in the literal meaning, of finance.

The otherwise firmly liberal Swiss economist Bruno Frey already suggested many years ago that executives should be paid like civil servants, because of the undesirable effects of pecuniary motivation. That applies for finance as well: An oath would not have strong effects as long as individual incentive systems always make pecuniary gains salient. But only changing incentive systems would also fail, because people will find ways to circumvent this. Hence, only a combination of both will change the system radically to the better.

Of course, an oath would just be the capstone event in a radically renovated system of finance education. It is not enough to add a ‘business ethics’ course to the traditional curriculum which just tells students how to make more money from money, camouflaged as ‘risk transformation’, but ending up as risk production. Finance education must include a strong dose of transdisciplinary topics, history, philosophy and law. The technical aspects, after all, will soon be left to AI anyway!


Akerlof, George A. and Robert J. Shiller (2015): Phishing for Phools: The Economics of Manipulation and Deception. Princeton and Oxford: Princeton University Press.

Cohn, Alain, Ernst Fehr and Michel André Maréchal (2014): Business culture and dishonesty in the banking industry. Nature, 19,

Herrmann-Pillath, Carsten (2019): Plädoyer für einen Aristotelischen Eid im Finanzsektor, in. Graupe/Ötsch/Rommel, Hrsg., Spiel-Räume des Denkens. Festschrift zu Ehren von Karl-Heinz Brodbeck, Marburg: Metropolis 2019

Zingales, Luigi (2015): Presidential Address: Does Finance Benefit Society? The Journal of Finance 70: 1327–63.

What is Social Neuroeconomics?

At the INSOSCI book symposium we discussed various suggestions for the title of our volume of collected papers that will be published by Routledge. We ended up with: ‘Social Neuroeconomics: The Integration of the Neurosciences and the Social Sciences’. So, what is Social Neuroeconomics?

There is already an occasional use in the literature of that term, but the meaning is not settled. This can be found in the specific context of neuroeconomic research on social preferences (Fehr and Camerer 2007). This research has produced the insight that social preferences may be rooted in mechanisms of choice that correspond to individual preferences in activating the same dopaminergic reward circuits. That means, acting with a social orientation produces rewards in the same way as, say, experiencing satisfaction from consuming positively valued goods. Hence, the motivation for using the term ‘Social Neuroeconomics’ is that the analysis of social behaviour builds on the basic neuroeconomic model of choice.

It is remarkable that this view seems to reinstate Adam Smith’s notion of ‘fellow feeling’. In his ‘Theory of Moral Sentiments’, Smith distinguishes between ‘sympathy’ and ‘fellow feeling’. Sympathy is very similar to the modern term ‘empathy’, especially in the cognitive meaning, and may be conceived as a capacity to generate social preferences: Sympathy enables us to take the position of others, and thereby develop moral judgments that take into account their interests. Sympathy does not mean that we really ‘feel’ like others: We can imagine the pain of others, but we do not feel that pain. However, we have ‘fellow-feelings’: That means, we enjoy the plain fact that we can ‘sympathize’ with others. That means, if we sympathize with their pain, that goes along with a positive feeling (even though pain is a negative feeling for the other). That seems very similar to the modern neuroeconomic analysis of rewards gained from socially oriented behaviour.

We consider this use of the notion of Social Neuroeconomics as too narrow: That follows from well-known criticism of neuroeconomics in focusing too narrowly on mechanisms of choice, thus following a similarly narrow definition of economics as a science of choice: That is dubbed ‘neuroclassic’ analysis in Camerer’s (2013) review of Glimcher’s ‘ Foundation of Neuroeconomic Analysis’. In the alternative view, neuroscience would also contribute to rethink standard conceptions of economics, as it happened with behavioural economics and psychology, too. Indeed, the intellectual field is complex and messy: Neuroeconomists often do not support the views of behavioural economists, as far as the standard model of choice is concerned. For instance, they often refute the ‘dual systems’ approach that many behavioural economists maintain in opposition to the standard economic model.

Our concept of ‘Social Neuroeconomics’ differs fundamentally from this narrow use, although it can include it, in the Smithian sense. To a certain extent, we approach it as integration of social neuroscience and economics, and even beyond this, as an integration of the social sciences and the neurosciences in dealing with economic phenomena. The integration often allows for better and more adequate theories of social and economic phenomena, and it occasionally leads to a substantial reconception of the previously described phenomena. At the same time, the social and economic context is often indispensable for the identification, localization and understanding of specific brain mechanisms. Their specific role remains opaque if not related to a social or economic environment. In this sense, what we call Social Neuroeconomics is a general methodological approach that has multiple directions of explanation.

One case in point is the analysis of emotions (which Camerer also emphasizes). Emotions do not play a prominent role in neuroeconomics as practiced so far, especially in terms of foundational theoretical concepts. In our understanding of social neuroeconomics, we would assign emotions a central place in the theory, as it is done in social neuroscience. The theory of emotions often goes along with a modular view on the brain, rejecting ‘general purpose’ rationality as a model for mechanisms, as in the theory of choice. Another important difference is the explicit recognition of the flexibility and context-dependence of neuronal mechanisms: This implies the analysis of media that connect the brain with its social environment, such as language. In both cases, ‘social neuroeconomics’ would not simply mean that neuroeconomics, as it stands, is now applied on social phenomena, but that a genuine integration of social science theories and neuroscience would be aimed at when understanding certain behavioural phenomena in the economy.

Let me give another example from the INSOSCI workshop. One of the headline-making insights from early neuroeconomics was the role of oxytocin in triggering trust among people. Trust, after all, is an important concept in understanding successful economic interaction and cooperation. In her contribution, Carolyn Declerck analyses the new literature and own research on the ‘metafunctionality’ of oxytocin which reveals that it can work both in a pro-social and an anti-social way, depending on how test persons perceive their environment, especially along the lines of in-group/out-group distinctions and degrees of perceived threats from others. Clearly, this implies that in order to understand the role of oxytocin in the real world, we would have to include a theory that explains the emergence of such perceptions in the social environment. One cannot reduce the phenomenon of trust to the level of neuronal phenomena but needs a combination of neuronal and social mechanisms in order to achieve a full explanation. This is where the multi-directional character of what we call “Social Neuroeconomics” research becomes visible.

We believe that philosophy has a role to play here, and this belief is a major aspect in the book that comes out of the INSOSCI symposium: Genuine cross-disciplinary integration presupposes reflection on the way how various disciplines define their methods and design explanations, and to figure out how conceptual bridges can be built. We think that mechanistic philosophy of science offers a promising starting point.


Camerer, C. 2013. A review essay about Foundations of Neuroeconomic Analysis by Paul Glimcher. Journal of Economic Literature LI (4): 1155–1184.

Fehr, Ernst, Camerer, Colin F. (2007): Social neuroeconomics: the neural circuitry of social preferences, Trends in Cognitive Sciences,  Volume 11(10): 419-427

Who is afraid of final causality?

A couple of days ago, we successfully finished our capstone INSOSCI book symposium bringing together neuroscientists, philosophers, economists and social scientists to discuss what emerged as the core topical concern: social neuroeconomics, the integration of the neurosciences and the social sciences under the auspices of economics. In my own contribution, I introduced the argument that semiotic mechanisms may provide the missing link between neuronal and social mechanisms. In this context, I argued that we need to adopt a broader concept of causality, as I already did in my ‘Foundations of Economic Evolution’ (2013). This is inspired by Aristotle (again! – see previous post).

Our INSOSCI collaborator Jaakko Kuorikoski commented that he feels ‘scared’ about introducing final causality, a stance shared by many philosophers of science. I do not agree, of course. To the contrary, I think that many problems of integrating the sciences with the social sciences and the humanities result from the fact that most scientists and philosophers of science reduce causality to efficient causality. If you do that, you get many other scaring things: mind / matter dualism, homunculi, emergence, first-person experience and consciousness as a ‘hard problem’, you name it. My position is very close to Terrence Deacon’s as elaborated in his book ‘Incomplete Nature’. Deacon makes a simple point: We have been socialized in the Western world that if we look for causes, that should be things that ‘exist’. One chapter has a telling quote from Lao Zi, who argued that what makes a vessel useful is emptiness, hence what not exists. Most generally, causes that do not exist are of two classes: constraints (which exclude possibilities from realization) and directions (towards realizing a possibility that has not yet materialized). Deacon introduces a new term for this type of processes or process characteristics: ‘ententional’ processes. Interestingly, this makes the argument even more scaring for our friend, I guess: Now even Aristotelian formal causality is added!

I do not want to elaborate on this in detail here but let me just fix the basics. Jaakoo is scared because he simply equates causality with efficient causality, as most people do. Obviously, a ‘final efficient causality’ is a non-starter. Something that does not exist cannot efficiently cause anything (a caveat: There is a substantial analytical philosophical literature about omissions or non-occurrences as causes). But Aristotle’s idea of causality was much broader. In simplest terms, we search for a cause when we ask the ‘why’ question. Think of an engine, a literal ‘mechanism’. We can look at this in terms of efficient causality, analysing the flows of fuel and the chemical reactions happening in a complex arrangement of mechanical parts that mechanically work together which ultimately results in movement. This is one answer to questions such as ‘what makes the car moving ahead’: Well, the engine is running. But we can extend our perspective. The analysis of efficient causality does not really account for the causal role of the design of the engine. Indeed, that matters much in mechanistic explanations as we employ it in the INSOSCI context: mechanisms are constituted by parts in a specific arrangement, which must be in place simultaneously (if not, the mechanism breaks down and nothing happens). This is a fundamental difference to efficient causality that is flow of events in time. In our case the arrangement is the design of the engine. Design is an example for formal causality. When dealing with ‘mechanisms’, Aristotle would certainly say that the mechanism as such is an instance of formal causality. Thus, I would argue that the difference between mechanistic explanations and other explanations involving efficient causality is exactly this: Mechanistic explanations include a role for formal causality. Think of a fundamental problem in all mechanistic analysis: What is the boundary of a mechanism? Since mechanisms are always are triggered by external causes, mere efficient causality does not allow for identifying that boundary (why should those causes not be part of the mechanism?). In other words, I think that standard mechanistic explanations combine efficient and formal causality, and that’s why they are not coterminous with the former.

The next question is, why does the mechanism exist? In the case of the engine, the Aristotelian answer is simply: by design. The engine was made by somebody. In the process of making the engine, design assumes the role of final causality. We can generalize, again. In analysing mechanisms, we always need to ask, what is the cause for the emergence of that mechanism, its stability, is recurrence through time and across space, and so on. In the context of the life sciences, this is the distinction between proximate and ultimate causes, and relates to the phenomenon of directedness of evolution and of the functionality of biological phenomena. Both functional and evolutionary explanations are instances of final causality, in my view. If we want to explain animal behaviour, we analyse this in terms of functions, and these are ultimately referred to analytical categories such as adaptation and reproductive success.

In my view, evolutionary explanations rely on final causality. This is not scaring because this relates to the role of emerging constraints in channelling evolution, hence endogenously creating directedness. If you look at evolution only in terms of efficient causality, you would only see the part played by randomness (which worries many thinkers). But there is no designer. Design, however, means constraints. Thus, the point is that constraints emerge and stabilize endogenously. This process is covered by the notion of final causality.

Deacon argues that this logic can also be applied on the mind/brain problem, thus enabling us to construct a physical theory of mind that nevertheless can account for the specific properties that we normally assign to the ‘mental’. If this succeeds, indeed, we should not be scared about final causality, but regard it as an essential element in our philosophy of science!


Deacon, Terrence W. (2013): Incomplete Nature: How Mind Emerged from Matter. New York: Norton, 2013.





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