The Markets Can Only Be Navigated, Not Solved
Node 002 | The Index
The Idea INDEX
Node 002. (This Entry May Evolve)
First Published: 03.07.2025
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The Markets Can Only Be Navigated, Not Solved
By default, most assume the markets are complicated, like a bicycle. Instead, they are complex like a rainforest. From here springs all misfortune for the novice or endangers the experienced trader.
What are these terms? In a ‘complicated’ system, you can separate and recombine the parts, and you will not be surprised. 1+1=2. A complicated environment is linear; therefore, it is solvable and computable. Iteration and best practices win. That best practice never changes. This is because a complicated environment or system is static; it remains unchanged. Reducing a system to its individual parts (reductionism) and studying them individually is an effective method for understanding a linear, yet complicated system.
This also means that cause and effect are directly observable and predictable. Answering the question posed by the system does not alter the question being asked simultaneously. Causality is a one-way street. Incidentally, schooling, along with its assessment methods and much of mainstream economics and finance that have developed over the last century, implicitly and explicitly base their worldview on the idea that the world is a static and solvable—i.e. ‘complicated’ system or environment. Computation, not creativity, won the last two centuries, did it not?
Yet, the world, in its infinite multitudes, is complex, not complicated. In turn, so are the financial markets, which are a strange carnival mirror world that reflects our own. In a complex environment, there is only one certainty: change. You cannot separate its parts, and you will be surprised, brutally so, in the attempt.
Further, these parts of a complex system produce more than you can predict by observing each in isolation. That is: the sum of the parts is greater than the whole. A single water molecule is not wet yet aggregate enough together and ‘wetness’ as a property emerges. Similarly, consciousness does not emerge from a single neuron; yet, when enough neurons are aggregated, consciousness does emerge.1 At which exact point? Unknown. But you can anticipate that it will.
In a complex system or environment, there is another level or environment that exists on top of the lower one without formal ‘awareness’ of each other. Yet one can affect the other. Consider that you can’t predict traffic by looking at one car. (Would you even know that traffic is a phenomenon if you only studied one car?) Not even with two cars does traffic emerge; yet aggregate enough cars together, and suddenly ‘traffic’ emerges. The drivers are not aware of most other drivers, nor their cars, yet the mass of cars behaves (react to each other) as though the traffic has a mind, a ‘life’ of its own. (Is traffic aware of its inner workings? Alternatively, the cars and drivers do not have a complete understanding of the ‘guiding hand’ traffic god that has emerged out of their collective presence.)
Two key words have so far been repeated, which are used as identifiers of an environment: ‘pattern’ and ‘emergence’. Consider what Traders of Our Time relayed from John Holland’s introduction to complexity science in Chapter 13: The Sphinx (p.424):
John H. Holland (2014: 5–6)2 identifies “several kinds of telltale behaviour” of a complex system:
•self-organisation into patterns, as occurs with flocks of birds or schools of fish
•chaotic behaviour, where small changes in initial conditions (‘the flapping of a butterfly’s wings in Argentina’) produce later changes (‘a hurricane in the Caribbean’)
•‘fat-tailed’ behaviour, where rare events, e.g., mass extinctions and market crashes, occur much more often than would be predicted by a normal (bell-curve) distribution
•adaptive interaction, where interacting agents, e.g., markets or the Prisoner’s Dilemma, would modify their strategies in diverse ways as experience accumulates
And lastly, he writes: “emergent behaviour is an essential requirement for calling a system ‘complex.’” So is the complex behaviour of rainforests, social human interaction, traffic and much more described.
We have grouped some common identifiers to facilitate a mutual understanding of the real nature of the markets: they are a complex, non-linear, dynamic system that has a particularly peculiar and paradoxical feature: it can also observe itself, causing it to change. Further, as we said in Traders of Our Time’s Preface: Why Think Meta? “We act on what we expect future prices to be, and that drives today’s trading. Yet today’s trading is what actually creates those very same expected prices!” Cause-and-effect? What about effect-and-cause? Traffic affects the car, and the car affects the traffic. Or is it the other way around?
Causality is not a luxury afforded to those within a complex environment. That is because attempting to answer the environment’s question simultaneously changes the question! In other words, some participants anticipate cause-and-effect relationships, thereby altering the actual cause-and-effect relationships. Or reversing it entirely. If some drivers anticipate traffic, they take alternative or pre-emptive action, altering causality itself. They are a part of the traffic itself. And so, new behaviours, patterns, and secondary knock-on effects emerge. (Front-running the front runners, who wise up, so they front-run you, the front-runner!) Would you have ‘predicted’ all of this by looking at all the components—the engine, the wheels, the chassis—of a single car?
Therefore, reductionism (separating the parts to understand the system or a new environment) within a complex, not complicated environment is futile and often dangerous. It is the default mode of how we’ve been taught to learn, and the default way of being assessed is that we’ve learned these things. (‘The October 1917 Revolution happened because of A, B, or C?’) It is not surprising, then, that a reductive, “this is a complicated environment, let me find the answer” approach is also the default for the novice trader. This carries many downstream consequences, misassumptions and mismatched expectations.
[Extra brain twister: language itself is reductive. Labels and categories may be the wrong tools for discussing what a complex environment even is. But it’s the best we’ve got. Yet, language also exhibits complex traits; itself a sea of change. New language emerges, which is also a source of causality head scratchers. (Does behaviour alter language, or does language alter behaviour?) Furthermore, the language we use is also an emergent environment that builds upon the environment we are observing. Yet neither environment is aware of the other, yet they still affect each other. ‘Traffic’ has emerged as a word. “Let’s turn here to avoid the traffic,” changes the traffic.]
Yet, if you observe a complex system, even without predictability, total information, or a complete understanding of its inner workings, repeatable behaviour and phenomena do emerge at a higher level. As do the behaviour of ant trails around colonies, weather phenomena, traffic patterns, etc. (These examples are also complex systems or environments.) Foreshadowing some of the conclusion: you can’t compute the parts (solving the market bottom-up), but you can navigate the sea of change (as a human, best-guess navigator, i.e. top-down observation)
Further, sensitivity to initial conditions—known as ‘chaos’—is a feature of a complex environment. If you watch the traffic on the same stretch of road each day, even noticing how small changes (driver turns left today, not right like yesterday) can have a dramatically different outcome in behaviour for a variable period of time, itself unknown. Fat-tailed behaviour (where once-in-a-lifetime events occur regularly in markets) and adaptive interaction between participants are further interconnected reasons why change is perpetual, and you will be surprised.
A lack of change only reinforces the brutal rapidity of change when it does come. A complex behaviour can exhibit and behave as a static, complicated system for periods of time. Those periods can be years, reinforcing certain new behaviours and proclamations of a new paradigm. Consider the ZIRP, QE, and ultra-low volatility era that followed the European Debt Crisis, which made the markets seem as if they were complicated, therefore computable, because the multi-variable, dynamic world of the markets was reduced to a handful of variables. If computation wins, speed wins, and so began the dominance and mainstream awareness of the quant industry. Cue the proclaimed demise of the human trader. Low volatility begot low volatility, behaviours and ‘strategies’ optimised over each other to plan for the continuation of that stable, static, computable, complicated system. We all know what happened next.
A Trader Is A Navigator, Not A Solver
We return to the beginning: the sole certainty of the markets is change. That requires a persistent navigator, not a solver. Solving is a fixed state rooted in the past; navigation is to ask, “What’s next?” Misfortune occurs when the environment is misidentified and the wrong tools, methods, and mentality are applied to it. (NB: ‘tools’ and ‘method’ are precisely what doesn’t survive a complex environment. They break under the grind of change and time.)
“Probe-sense-respond” is a shorthand description by David J. Snowden and Mary E. Boone for navigating complex environments. This differs from “sense-analyse-respond,” which is best suited for a complicated environment. 3 Mismatching them is disastrous. A participant who figures it out as they go along will be trumped by the participant who has solved (computed the best or optimal action and reaction) within a complicated system. Take, for example, Chess—where the rules never change. Brute force computation has won. Vice-versa for a complex environment where the rules consistently change—gradually, then suddenly—and the figure-it-out-as-you-go-along participant wins by not being tethered to the old environment like the participant that has solved and optimised for it.
“Probe-sense-respond” is yet a great way to describe the skill set and action of an effective discretionary human trader, like The Sphinx said: “things construct themselves.” In other words, just like how the career and skills of a trader emerge over time, so does a singular particular trade in a moment in time.
Sense should not be overlooked. Consider ‘trade sense’ and the importance of it, as in Traders of Our Time. (Chapter 9: The Engineer, Part I):
What good are other-worldly skills in reading order flow, or infinite risk-tolerance, to have the guts to trade large sizes if the trader dogmatically and statically treats each market move as if the same? How is that trader better than an algorithm, a trader whose binary and static approach ignores the tiering of opportunity? ... the other part of trade sense, then, is bottom-up, which is to understand how the probabilities shift dynamically in the trade, and then to manage risk as a response proactively, and finally to evaluate the outcome of your actions and reorient again during a trade, while exposed to risk. The last point is where accomplishment ends and mastery begins: to understand how your own activity—adding, cutting, scaling size—has re-shifted the risk of your position while the probabilities of the market move have never stopped changing in the background.
Critically: trade sense is borne out of observation. A trader’s powers of observation and their confidence in them are critical, fundamental to everything, since only through observation can they track and navigate change. Change is the only constant of a complex environment. To survive and navigate a complex environment (change), the best trader is the best market observer, and nothing can come between.
As explored in ‘The Trader’s Original Sin Is Attempting To Make An Infinite Process Finite’, the more ‘layers of abstraction’ the trader puts between themselves and the markets (an imperfect reflection of an infinite process), the more they are doomed. Or to fork it: the more they put between themselves and their observations of the markets, the more they are doomed. Instead, trading mastery is a reduction of the layers, to ask: how reductive can I be for the markets to be comprehended (navigated) and no more?
That is also because each of these ‘layers’ is typically born out of the attempt to ‘solve’ the environment. A hardline rule is embedded into a trader’s behaviour because of one bad trading day. (“I will never sell Bund lows!”) Thought is outsourced to a system or ruleset for trading. (“Buy here when this crosses over.”) Or a heuristic held in high regard because the ‘professionals’ did it, countless outdated market cycles ago. (“Back then they just did X—why are you doing anything different?”)
Some further symptoms and questions:
1. Treating ‘trading’ as a ‘try-your-hand’ rather than thinking of developing a multi-decade career and the disparate, vast skills to support it. Comparable to showing up to the gym to do ‘random exercise’ to feel good, rather than using the gym as supplementary in the way an athlete uses a planned program to achieve a specific goal, or enhance base athleticism (periodisation.) Likewise, showing up to the markets casually is disastrous for your financial goals.
2. Can I even identify my navigation skills, or acknowledge that I’ve been spending time trying to solve rather than navigate?
3. Suppose I can identify these skills. How can I best develop them further, keeping in mind that markets can only be navigated, not solved? Has an implicit background assumption of trying to ‘solve’ crept in at any time in the past few years?
To Be Developed—Node 002 | The Idea Index
3rd July 2025, WIP: Reassessing and linking previous articles where the idea was briefly introduced. Further identification of symptoms of traders failing due to misidentifying the environment and asking the wrong questions, often caused by an ‘upstream’ issue that creates numerous downstream anxieties. How can this serve as a diagnostic tool, thereby aiding the trader in their development? There are many other implications to be unearthed, both upstream and downstream.
John H. Holland, Complexity: A Very Short Introduction (Oxford: Oxford University Press, 2014), 1–3.
Ibid.
David J. Snowden and Mary E. Boone, “A Leader’s Framework for Decision Making,” Harvard Business Review, November 2007, www.hbr.org/2007/11/a-leaders-framework-for-decision-making (accessed 3 June 2024)
Acknowledgements & Some Further Reading:
Gleick, James. Chaos: Making a New Science. London: Vintage Publishing, 1997.
Holland, John H. Complexity: A Very Short Introduction. Oxford: Oxford University Press, 2014.
Mitchell, Melanie. Complexity: A Guided Tour. New York: Oxford University Press, 2011.
Santa Fe Institute. “Applied Complexity Program Office.” Santa Fe Institute. https://www.santafe.edu/applied-complexity/office (accessed 2 July 2025).
The photograph, provided by Axia Futures, is used with their permission, and they retain full ownership and copyright over the image.