The Idea INDEX
Node 002. (This Entry May Evolve)
First Published: 03.07.2025
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For a list of further reading, please refer to the bottom of this node.
The Markets Can Only Be Navigated, Not Solved
Most assume the markets are complicated like a bicycle. Instead, they are complex like a rainforest. This brings misfortune for novices or endangers experienced traders.
What are these terms? In a ‘complicated’ system, you can separate and recombine the parts, and you will not be surprised. A complicated environment is linear; therefore, it is solvable and computable. 1+1=2. Iteration and best practices win. That best practice never changes because a complicated or static environment remains unchanged. Reducing a system to its individual parts (reductionism) and studying them individually is effective for understanding a linear, complicated system.
This means 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. Over the last century, schooling, its assessment methods, and much of mainstream economics and finance developed, implicitly and explicitly basing their worldview on 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. The financial markets, which are a strange carnival mirror world that reflects our own, are complex too. In a complex environment, change is the only certainty. You cannot separate its parts, and you’ll be brutally surprised in the attempt.
These parts of a complex system produce more than you can predict by observing each in isolation. 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 aggregate, consciousness does emerge.1At which point? Unknown. But you can anticipate it.
In a complex environment, there’s another level or environment above 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 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 (reacts 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 understand the ‘guiding hand’ traffic god that emerged from their collective presence.
So far, two key words have 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’ve grouped common identifiers to facilitate a mutual understanding of the markets. The markets are a complex, non-linear, dynamic system with a peculiar and paradoxical feature: it can observe itself, causing itself to change. 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. Attempting to answer the environment’s question changes the question! In other words, some participants anticipate cause-and-effect relationships, altering the actual relationships or reversing them. If some drivers anticipate traffic, they take alternative or pre-emptive action, altering causality. They are part of the traffic. New behaviours, patterns, and secondary knock-on effects emerge. (Front-running the front runners, who wise up, so they front-run you!) Would you have ‘predicted’ this by looking at all the components—the engine, the wheels, the chassis—of a single car?
Within a complex, not complicated environment, reductionism (separating parts to understand a system or new environment) is futile and dangerous. It’s the default mode of how we’ve been taught to learn and assess learning. (‘The October 1917 Revolution happened because of A, B, or C?’) A reductive, “this is a complicated environment, let me find the answer” approach is also the default for novice traders. This carries downstream consequences, misassumptions and mismatched expectations.
[Extra brain twister: language is reductive. Labels and categories may be the wrong tools for discussing a complex environment. But it’s the best we’ve got. Yet, language exhibits complex traits; a sea of change. New language emerges, revealing causality head scratchers. (Does behaviour alter language, or does language alter behaviour?) The language we use is an emergent environment that builds upon the environment we are observing. Yet neither environment is aware of the other, yet they affect each other. ‘Traffic’ has emerged as a word. “Let’s turn here to avoid the traffic,” changes the traffic.]
Yet, in a complex system, even without predictability, total information, or complete understanding of its inner workings, repeatable behaviour and phenomena emerge at a higher level, like ant trails, weather, and traffic patterns. Foreshadowing: you can’t compute the parts (solving the market bottom-up), but navigate the sea of change (as a human, best-guess navigator, i.e. top-down observation)
Sensitivity to initial conditions—known as ‘chaos’—is a feature of a complex environment. If you watch the traffic on the same road daily, even small changes (a driver turns left today, not right like yesterday) can dramatically change traffic 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 among participants are interconnected reasons why change is perpetual and surprising.
A lack of change reinforces the rapidity of change when it occurs. A complex behaviour can exhibit and behave as a static, complicated system for long periods, reinforcing new behaviours and proclamations of a new paradigm. Consider the ZIRP, QE, and ultra-low volatility era after the European Debt Crisis, which made the markets seem complicated and computable because the multi-variable, dynamic world of the markets was reduced to a handful of variables. Which means it can be calculated, therefore computed. If computation wins, speed wins, leading to 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 for the continuation of that stable, static, computable, complicated system. We know what happened next.
A Trader Is A Navigator, Not A Solver
We return to the beginning: the only 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. (NB: ‘tools’ and ‘method’ don’t survive a complex environment. They break under change and time.)
David J. Snowden and Mary E. Boone promote the shorthand “probe-sense-respond” for navigating complex environments, differing from “sense-analyse-respond” for a complicated environment. 3 Mismatching them is disastrous. A participant figuring it out as they go will be trumped by the one who solved (computed the best action and reaction) within a complicated system. In Chess—where the rules never change—brute force computation has won. Vice-versa for a complex environment where the rules change—gradually, then suddenly—and the figure-it-out-as-you-go participant wins by not being tethered to the old environment like the solved and optimised one.
“Probe-sense-respond” describes the skill set and action of an effective discretionary human trader. Like The Sphinx said: “things construct themselves.” Just like a trader’s career and skills, which emerge over time, so does a particular trade at a moment in time.
Sense should not be overlooked. Consider ‘trade sense’ and its importance, 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.
Trade sense comes from observation. A trader’s observation skills and confidence in them are critical, as only through observation can they track and navigate change. Change is the only constant in a complex environment. To survive and navigate this environment, the best trader is the best market observer.
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?
Each ‘layer’ typically arises from attempts 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. (“Buy here when this crosses over.”) Or a heuristic held in high regard because the ‘professionals’ did it, outdated market cycles ago. (“Back then they did X—why are you doing anything different?”)
Further symptoms and questions:
Treating ‘trading’ as a ‘try-your-hand’ rather than developing a multi-decade career and the necessary skills is disastrous for your financial goals. This is comparable to showing up to the gym for ‘random exercise’ to feel good, rather than using it as supplementary like an athlete’s planned programme to achieve a specific goal or enhance base athleticism (periodisation). Approaching the markets casually is disastrous for your financial goals.
Can I identify my navigation skills, or acknowledge that I’ve been trying to solve rather than navigate?
If I can identify these skills, how can I develop them further, considering markets can only be navigated, not solved? Has an implicit assumption of trying to ‘solve’ crept in recently?
»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., 5-6.
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.