The Market Mind Hypothesis, page 32
These discoveries again? Yes. Besides benefitting from contraries (like those between S1 and S2; see below) human progress has largely been achieved by imagination. So, in addition to the creative tension between contraries, it is imagination that leads to innovation and technological breakthroughs. Imagination involves not only thinking about what could be, in a conceptual way, but also—and more importantly—how that might feel like. This involves both space (a new environment) and time (a new era). It is this experience of a possible future as distinct from the current ‘world’ that motivated people to reach beyond what was available at the time and facilitated them to focus on interim targets and goals. Crucially, imagination often occurs without external inputs. In other words, not only does it lack physical ‘big [future] data’, there is also no sense organ for time like there is for sound or touch.
6.2
The Case of Mind as Complex Adaptive System
To paraphrase Hayek, Gödel-Turing’s broader relevance is that not assuming limits on computation implies that there is in principle no reason why all observed price patterns and other economic forms (like innovations) cannot be achieved by a central command with a powerful computer. Instead, it is in the true nature of economies as complex adaptive system to evade capture by any algorithm or mathematical model. Any complex adaptive system has to produce genuine novelty, with insights as internal surprises (even to itself), to ‘unpredictably’ deal with external ones, including threats by adversaries. And for that it needs the freedom to discover. In that regard, although this matter is not settled and divergent views remain, three clear understandings have emerged in the field of complexity science over the last few decades.7
First, that the sine qua non of a complex adaptive system is discovery. Specifically, it generally is not, as is often assumed, the non-linearity or chaos it exhibits but the aforementioned endogenous ability to internally produce surprises to deal with external surprises. In the case of a conscious system, this can be to fight (via “A-ha” insights that can lead to innovations or novelty) or flight (via “Oh-no” realisations). This lies at the core of such systems’ self-organisation: to coordinate behaviour and realise order in the face of chaos.
Second, that the dynamics involve both competition (like the Red Queen principle; ‘running to stand still’) and cooperation (like alliances and assemblies), ultimately realising a unity or even synergy of opposites. Such conjunction reflects that the very opposing of—say, by tension between—its elements is a diversifying strength at the systemic level. Progress by innovation thus comes with leaps and bounds—widespread acceptance and adaptation versus fierce rejection and disruption—produced by parallel and counter moves by other parties. We can observe this both in nature and the economy. In short, our conjunction requires a yin~yang kind of dualist structure that is seemingly oppositional but practically complementary. Specifically, the conditions of such opposition, combined with the recognition of its existence by each opposing force, can be shown to be logically necessary for “emergence” as Kauffman points out: “coevolution of entities which interact with and know one another. The laws which govern the emergence of knower and known … lie at the core of the science of complexity” (Kauffman, 1991, p. 1, emphasis added).
Third, as a consequence of their emergence, certain properties of this system escape reduction into an axiomatic description and its resulting innovations are beyond algorithmic enumeration or computation. This emergence points back to reflexivity. In a system where the analysis of a situation is a function of exactly that analysis—e.g. an expectation of its outcome determines that very outcome—there is no logical or deductive way to settle this, and some form of meta-cognition is required to break this loop. For example, how did Soros know, i.e. recognise patterns, when he was participating in shaping them? He experienced back pain: “I basically have survived by recognizing my mistakes. I very often used to get backaches due to the fact that I was wrong. Whenever you are wrong you have to fight or [take] flight. When [I] make the decision, the backache goes away”.8
At this point you may wonder what the argument is to consider the mind as a complex adaptive system in the first place. In particular, what makes up the required oppositional structure and how can the mind endogenously generate novelty, the pre-condition for adaptive self-organisation? I hinted at the answer before which lies in the intrinsic opposition between the unconscious (S1) and deliberate (S2) forces which is played out in symbolic dynamics. The relationship between these can be seen in terms of the complex dynamics between competition and cooperation. Complex psychology emphasises the autonomy of the unconscious and assigns intelligence to it which feeds intuition and even embeds an element of prognostication.9 Similarly, Gigerenzer starts his bestseller Gut Feelings: The Intelligence of the Unconscious as follows:
We think of intelligence as a deliberate … activity guided by the laws of logic. Yet much of our mental life is unconscious, based on processes alien to logic: gut [instincts], or intuitions … We sense that the Dow Jones will go up … Where do these … come from? (Gigerenzer, 2007, p. 3)
Consequently, my proposition is, first, that the unconscious and deliberate forces can be considered as ‘intelligent’ agents’10 in terms of the Gödel-Turing framework. Second, the opposition and resulting tension between these two subsystems, combined with their mutual recognition at their own respective level of this opposition (that is, they agree to disagree), are the necessary conditions for innovative outcomes to adapt and progress in conjunction, particularly in times of crisis. However, that conjunction and the eventual release of the tension never take place at their own respective levels but occur in S3.
As discussed in Appendix 1, the feeling system S3 complements and feedbacks to the unconscious system S1 and the deliberate system S2. This feedback links nicely, first, to Soros’ reflexivity which recognises that statements about reality are false, true, or reflexive. In turn, we can judge this in our Gödel-Turing framework. For arguments sake, and to keep it simple, suppose we agree with the consensus in behavioural economics: S1 generally makes ‘false’ statements about reality (bad decisions) and S2 makes ‘true’ statements (good decisions). Here we invoke Gödel’s second incompleteness theorem which tells us that the provability of these statements can only be done outside this dual-process system. “Duh”, I hear you say: their proof requires facts from the real world (which may be delayed). So how does this fit within our mental triple system? S3 makes reflexive statements in terms of experiencing such proof: as information (i.e. facts) dually realised. These experiences are fresh and novel. They are a form of discoveries and insights. They are, in Gödelian terms, new statements added to the existing axioms of our dual-process system. S3 fills the gaps left by the ‘inconsistent’ S1 and the ‘incomplete’ S2. I fact, I submit that the proof of emotions, thoughts, and other statements as realisations in S3 has two phases:
In consciousness, by being aware of them, they become true in the sense of being ‘switched on’ (True = 1) and ready for step 2. For example, a foreboding emerges from your unconscious (S1). Until then it was not available for provability. Or your recently produced analysis (S2) only now makes sense, i.e. it feels right, making it ready for provability.
In consciousness, by sentience, they are ‘fact’ checked against the reality of the outside world. These can be powerful feelings. For example, Bob Geldof felt elated after his emotional appeal for help resulted in the unique Live Aid success (proving his S1 was right). And Nikita Khrushchev felt relieved after his rational backing-down in Cuba prevented a war (proving his S2 was right).
In short, S1 and S2 statements can only be proved outside their system, namely in S3. Only S3, by way of our senses, is in direct contact with the real world to realise facts. It is there where knower and known meet, e.g. via our reality checks. And only then can we fully learn.
Intuition (S1) is gut instinct rather than brainy thought (S2). By becoming aware (S3) of bodily feedback and/or brainy signals the self-referential loop is transcended: you now know in your heart. By recruiting the gut, intuition is the mind’s primary tool to extend the brain, reach out and invite surprises,11 the unexpected unknowns, through discovery. It includes inspiration, for example to deal with true uncertainty, as expressed for example by poet Wisława Szymborska in her Nobel acceptance speech: “Whatever inspiration is, it’s born from a continuous I don’t know”. It also includes, as discussed, imagination with which the mind extends into possible futures. This leads to the insights the mind is searching for while exploring the unknown. They are the A-ha Erlebnisse in the eureka moments (see Harman and Rheingold, 1984; Klein, 2013; Kounios and Beeman, 2015). These are not exclusive to entrepreneurs or scientists. Dutch actor Rutger Hauer, famous for playing the robot Roy Batty in Blade Runner, stated it as follows: “I have that with all important things in life, I get like a flash of lightning, a dazzling insight” (Van Basten Batenburg, 2008, p. 53).12 They are crucial as they form the phenomenal overlay which enriches (i.e. values) dual-processes with (e.g. aesthetic) meaning, reaching beyond their initial impressions. Ultimately, in the words of Derman, echoing Kauffman, such experience “is a merging of the understander with the understood” (Derman, 2009, p. 5).
I have more to say on this in the next chapter, including a deep dive into price discovery. First, we need to explore the philosophical territory of numbers as symbols. Not so much in terms of Gödel’s numbers but much more in terms of “mathematical primal intuition which expresses itself, among other ways, in arithmetic, in the idea of the infinite series of integers, and in geometry, in the idea of the continuum” (Pauli, 1954, p. 149). Crucially, the numerical ‘coordinate’ (or scale) dimensions in which complex dynamics appear (e.g. in space-time: length, breadth, width, and duration) are important philosophical considerations when problems of complexity in general are analysed from a mind~matter perspective. I realise that the next few paragraphs will make some readers (even more) uncomfortable, if not disagreeable, but they are a necessary prelude to Chapter 7.
6.3
Symbols
In his discussion of value and prices, Simmel makes the following Pythagorean observation of economic exchange:
Only if there is a second object which I am willing to give away for the first, or vice-versa, does each of them have a measurable economic value. There is originally in the world of practice no single value, any more than there is originally in the world of consciousness a number ‘one’. It has often been asserted that the concept of ‘two’ exists prior to the concept of ‘one’. (Simmel, 1907, p. 89)
This leads us into our discussion of numbers (especially as symbols) in the economic system. Let’s start with digitisation which is based on the core digits 0 and 1. On the plus side, with numbers as symbols digitisation could offer ways to help bridge mind and matter. However, as the culmination of mechanisation it has practical implications and could backfire. Digitisation creates an intangible space, be it online or virtually. As closet dualists we perceive our reality to consist of the mental and the physical which complement one another. Still, as soon as causation is expected to rely on mentalities (including intangible tools used by those mentalities) we are moving closer to, what Knight (1921) calls, “true uncertainty”. As discussed, true uncertainty is due to the mind~body problem that extends into the economic system. For example, a tech venture that is intangible by operating online (in digital space), primarily relies on mentality (hope and hype) and operates via intangible tools (like digital apps) to achieve something is beyond Knight’s “risk”. So is investing in or lending to such a venture, especially if the lender is, similarly, a fintech bank whose business consists of electronic deposits and withdrawals of money.
On that note, and importantly, if money itself has no longer any physical property (e.g. as bill, coin, let alone gold) all that remains as ‘real’ is its number, namely as price. Similarly with algos and tech which both revolve around those core digits 0/1 as their essence. Combined with banking and investment apps this means that exchanges (like a central bank ‘printing’ money, a retail investor trading, or you withdrawing your deposit) are just clicks away as the only physical effort. And a bank run thus no longer consists of physical queues at branches but has become a digital event. FTX13 and SVB are cautionary tales for this. Specifically, what they have in common is that they operate in the extremes of this intangible space.
An important lesson in that regard is the connection between price discovery and the discoveries more widely in the economic system. They form a reflexive chain of creativity. All are made in minds—often socially, perhaps after collaboration—and initially appear symbolically.14 Here is Pauli sharing another thought:
When one analyzes the pre-conscious step to concepts, one always finds ideas which consist of “symbolic images”. The first step to thinking is a painted vision of these inner pictures whose origin cannot be reduced only and firstly to the sensual perception but which are produced by an “instinct to imagining” and which are re-produced by different individuals independently, i.e. collectively … But the archaic image is also the necessary predisposition and the source of a scientific attitude. To a total recognition belong also those images out of which have grown the rational concepts. (Pauli, 1948b; emphasis added)
In his advocacy for the Extended Mind Theory, including criticism of individualism/internalism, Wilson makes two cases: “the first involves the causal integration of explicit symbols located in an organism’s environment into that organism’s cognitive regime; the second appeals to the cognitive incorporation of non-symbolic aspects of that environment” (Wilson, 2010, p. 181). In turn, these support the MMH, in that the first underlines the role of prices (in the external pricing system) as symbols for investors, whereas the second underlines the role of the market’s exchanges. In this subchapter, as well as in the next chapter, I will primarily focus on the first case to support the idea of the market mind as extension. Specifically, Sutton (again, underlining the mind-as-market by casually using economic terms in describing cognition) argues that:
External symbol systems … are not always simply commodities, for the use and profit of the active mind: rather, in certain circumstances, along with the brain and body that interact with them, they are (part of) the mind. (Sutton, 2010, p. 190).
The mind as complex adaptive system is creative. It has to be, particularly in situations that are uncertain (which most are in our daily lives), where analytic problem solving (via S2) doesn’t apply. Symbols can help, often in a dynamic and iterative way, to generate the required insights. In their excellent book The Eureka Factor (2015), neuroscientists John Kounios and Mark Beeman relate the following experiment by the social psychologist Michael Slepian on the difference between insight problems and analytic problems. Ahead of a lab test, consisting of two groups of subjects, the experimenter apologised that the room was a little dark, so he switched on a light. For one group the light was a traditional incandescent bulb. They subsequently solved more insight problems but not more analytic problems. For the other group the light was a fluorescent tube. This had no impact on their performance in solving either of these types of problems. “Only the classic symbol of creativity [i.e. a lighted bulb] spurred insight” (Kounios and Beeman, 2015, p. 187).
The mind generates novelty in the form of insights which results in a vast array of symbols. And as we saw: “opposites never unite at their own level … since the symbol derives as much from the [deliberate] as from the unconscious, it is able to unite them both, reconciling their conceptual polarity through its [physicality] and their emotional polarity through its [phenomenality]”. (Jung, 1951, para. 280). In their very competing the healthy mind’s agents offer balance and diversity, uniting like a robust portfolio in the system’s broader adaptive purpose to produce novelty. The intercourse of the unconscious and the deliberate produces (realised via S3) their conscious child, the insight, “the birth of a third and new thing, a son who resolves the antagonism of the parents and is himself a ‘united double nature’” (Jung, 1956, para. 22). I will shortly zoom in on this “third” (which, of course, is a symbol itself).
With the Taoist yin~yang symbol as an example, the complementarity of cognitive opposites more generally (e.g. Atmanspacher and Primas, 2006) is closely associated with concepts in complexity science. My focus here will be on (numerical) symbols and information. There is the limited case of the Boolean True/False logic (1/0) of the Liar’s Paradox. Another is from Algorithmic Information Theory where it concerns the signal-noise dichotomy as captured in the symbol which embodies both. There are two important points to highlight in this regard.
First, information is always intentional (about something) and implicitly dynamic (it arrives, is produced/consumed). A signal, in that respect, is the intermittent alerting message of a pattern which is ‘in formation’. For example, the message to ‘pay attention’ or ‘be aware’ is one of the signals of the symbol as it emerges in consciousness. Noise, on the other hand, is the ever-present infinite ‘background clutter’ of the unknown, entropy’s disorder if you will. Although a symbol contains some information, primarily its signalling property, its wider meaning is discovered, a process which reflects a large part of uncertainty (i.e. chance encounters). In the context of evolution, Damasio (2004) argued that the discovery of new things by chance is required before selection can take place. We can therefore state that a symbol is a signal enriched by noise, in the sense that the informational tendency—as in ‘probability’—reflexively emerges from indeterminate randomness.15 To further clarify what I said before, Nietzsche famously remarked that certainty is what drives one insane. Translated in terms of the requirement as a complex adaptive system, the healthy mind must strategically use, almost embrace, indeterminacy to ‘surprise’ itself (with insights) in order to surprise (hostile) others. In short, inner surprises are a quid pro quo to outer surprises.
