Decision Making in Mycelium R&D (Overfitting as a Behavioral Construct)
Fit as a Behavioral Function
In modeling, fit describes how well a function captures the signal hiding inside a scatter of data points. A perfect fit would trace every observation precisely, but perfection is a red herring: a model that memorizes noise stops learning. Statistically, we call that overfitting; a curve so responsive that it explains everything and therefore explains nothing. The opposite error, underfitting, occurs when a model is too rigid to notice structure that is truly there.
Fundamentally, the concept of fit extends completely to the (very human) decision making component of R&D work. Every experiment, design decision, or parameter adjustment is an act of fitting, an attempt to map relationships with product, economic, and personal implications. Our behavioral fits are shaped not only by data but by psychological pressure: deadlines, resources, managerial expectations, and the pervasive need to feel progress. Under these conditions, it becomes painfully easy to confuse responsiveness with speed.
Over-fitted decision-making feels fast. Each result sparks movement; every new data point is met with a pivot or a plan. The conference room and the lab may hum, but that sensation of velocity is often an illusion, the cognitive dissonance of motion mistaken for momentum. Only in retrospect, when the path is traced, does the shape of the journey become revealed as a long, looping route that touched every point but advanced little toward understanding.
Under-fitted decision-making feels safe. It resists the noise by refusing to listen to it. The comfort of protocol replaces the uncertainty of exploration. But this efficiency is equally deceptive; it saves time by forfeiting learning, mistaking order for insight and linearity for progress. Both tendencies stem from the same behavioral source: our instinct to manage uncertainty through action or avoidance. The tension between flexibility and discipline (between reacting and listening) defines not just how we fit data, but how we fit ourselves to problem spaces defined by psychology, economics, and the drive for safety as much as the biology and technical character of mycelium.
In mycelium R&D, the stakes of that tension are amplified. The system itself is noisy, historical, and alive. Each run carries the residue of the last. To work effectively with fungi, then, is to recognize that fit is not only a statistical property but a behavioral one: a pattern in how we interpret noise, how we define progress, and how we decide what it means to move fast.
Behavioral Overfitting (The Lure of the Outlier)
If statistical overfitting is the tendency to chase noise, behavioral overfitting is the tendency to chase novelty. In R&D we learn early that exceptions attract attention; a single run that performs spectacularly can dominate the conversation. It becomes the bright pixel in the image.
There are good reasons for this impulse. In innovation and venture environments, the rare outlier can be the difference between obscurity and discovery [Malenko et al., 2023]. Early commitment to unusual signals has built entire industries; in fat-tailed spaces the tail can be everything. It’s easy to be convinced that moving quickly on anomalies is how breakthroughs happen (and sometimes that’s true), but particularly in mycelium systems, the danger is subtler. Outliers may not arise from randomness alone; they often sit on narrow ridges within the global response surface as a fragile intersection of parameters that the organism may never naturally revisit.
These ridges can appear stable in the moment, and may be technically reproducible under precise conditions but practically elusive: sensitive to history, microclimate, or even spatial patterning. They seduce because they seem coherent, not chaotic. Yet designing around them risks building a process that performs only within that ridge, blind to the broader terrain of the phenotype. In that sense, the outlier is not an error but a local truth mistaken for a general one. What looks like speed becomes a slow detour, a line drawn through a ridge instead of a landscape.
The seduction of outliers is amplified by culture. Organizations under pressure to show results often claim to be results-oriented, yet many operate more like hero factories. They reward visible exertion (long hours, emergency pivots, struggle) more reliably than they reward steady, reproducible progress. In these environments, over-fitted decision making thrives because it looks courageous. The person who jumps on the anomaly appears decisive, while the one who pauses to test its stability risks seeming hesitant.
This misalignment creates a subtle distortion in collective learning. When heroics overshadow results, the organization starts to select for reaction rather than reflection. Each dramatic save becomes part of its mythology, crowding out quieter forms of rigor. The hero narrative frames the anomaly as a proof of genius instead of a prompt for replication. It feels fast because everyone is moving even when the system’s understanding drifts, risking endless iteration around misunderstood success.
Overfitting in behavior, like overfitting in data, emerges from a misunderstanding of variance. In data, we mistake random deviation for pattern; in teams, we mistake urgency for learning. Both produce the same illusion: that responsiveness equals progress. What’s actually happening is a form of epistemic inflation; more decisions, less information. Recognizing this pattern doesn’t mean abandoning responsiveness. It means reframing what we celebrate. The most meaningful velocity in R&D is not how quickly we chase the next anomaly but how efficiently we convert uncertainty into understanding that resolves durable and demonstrable results. Moving fast may feel heroic, but learning fast is what actually moves us forward.
Behavioral Underfitting (The Comfort of Rigidity)
If overfitting is about moving too much, underfitting is about outright ignoring motion. Where the over-reactive lab rushes toward every new result, the under-reactive one clings to procedure. Both mistake security for understanding, but underfitting hides its inefficiency beneath the language of discipline. It presents as calm, ordered, and professional; everything a mature and effective organization longs to be.
Behavioral underfitting often organizes around self-protection; a retreat into what is measurable and reproducible [Barnett et al., 2000]. Protocol replaces curiosity, and deviations may be framed as risk. The process becomes a fortress: stable on paper, stagnant in practice. Each run confirms only what the last has already proved, where ‘optimization’ degenerates into repetition with tighter margins.
In a living system like mycelium, rigidity carries its own kind of noise. Fungal phenotypes drift across time and context; they adjust morphology, density, and structure in response to gradients or inputs that may or may not be visible. When development strategy refuses to flex with that drift, we trade interpretability for comfort. The system continues to change, but our language for describing it freezes in place. We stop asking what the organism is expressing and focus instead on how consistently we can reproduce a single expression, despite whether that single impression actually represents the best solution within the total phenotypic space.
Underfitting also thrives in organizations where efficiency is mistaken for clarity. Schedules, dashboards, and standardized forms create the feeling of progress (motion, not progress, converted into metrics). But the metrics may become detached from the underlying biology; a program can hit every deliverable and still be learning nothing. In those settings, underfitting becomes institutional: exploration is quietly re-labeled as deviation, and curiosity as inefficiency.
The cultural reinforcement mirrors the heroism that sustains overfitting. If the over-fitted engineer earns praise for reacting boldly, the under-fitted one earns safety through compliance. Both are rewarded for behavior rather than understanding, resulting in a developmental culture oscillating between crisis and control, mistaking consistency for stability and management for mastery.
To drive on an analogy, fungi maintain structure through flow not fixation. They build memory into morphology but keep growth responsive. Each branch carries history yet remains sensitive to its environment. The under-fitted development program forgets this as it holds its parameters constant while the organism keeps evolving beneath them. Avoiding underfitting doesn’t mean abandoning rigor; it means practicing rigor that can move and breathe. It requires processes that treat deviation as information, not failure, and metrics that reward the discovery of variance as much as its reduction; to understand the envelope of possibility.
If behavioral overfitting wastes energy chasing ghosts, behavioral underfitting saves energy by starving understanding.
Behavioral Right-Fitting (Adaptive Decision-Making and Cross-Validation)
Ideally we find ourselves on a narrow path between the reflex of overfitting and the inertia of underfitting: right-fitting that moves with uncertainty rather than against it. In modeling, cross-validation tests whether what appears to fit in one subset of data also generalizes to others. In R&D, the same principle applies not just to experiments but to behavior itself. A team or individual is well-fit when their reasoning continues to hold under new contexts, new stresses, and new information.
Right-fitting is a learning architecture that recognizes progress comes from iteration across folds of experience rather than from any single outcome. Each experiment becomes a partial view into the larger function, and the goal is not to find the flattest line through but the most transferable understanding across conditions. The same logic that governs cross-validation in a model governs sustainable progress in a lab: measure generalization, not confirmation.
Behaviorally, right-fitting begins with temporal cross-validation, which allows time itself to test conviction. In fungal systems, temporal replication is especially powerful because the organism’s history influences its present. Growth cycles reveal whether a result is stable across phases of memory and drift. In this sense replication and patience are the experimental form of humility. In contrast to the illusion of speed that defines overfitting, temporal patience measures how well we can maintain direction without immediate reinforcement.
The next layer is social cross-validation; testing understanding across people. Independent teams or colleagues repeating the same process are not redundancies, they are behavioral folds in the learning model where their deviations reveal the texture of the true function. Culturally, this requires rewarding replication and skepticism as much as originality and heroism, and cultivating a collective willingness to examine how we arrived at a result, not only that we arrived. Celebrating outcomes without reflecting on the path that produced them risks codifying error into process. Right-fitting organizations learn to look backward without blame: to surface the hidden variables in their own decisions, to recognize where intuition was wrong, and to convert missteps into pattern recognition.
The third dimension is emotional cross-validation as the ability to test our motives before acting. Over-fitting often arises from stress or ego: the desperate need to be right, to move fast, to prove control, or to simply survive. Emotional validation means pausing to ask whether a decision stems from clarity or relief. It also means making space to acknowledge when prior decisions were driven by those same pressures where reflection on failure is not contrition, it is calibration. Teams that can revisit their own histories honestly, without defensiveness, are the ones that expand their capacity to generalize insight.
Velocity, Not Motion (Right-Fitting as Practice)
Right-fitting therefore depends on managing perception as much as parameters. We must distinguish feeling fast from being fast, and activity from learning velocity. The true measure of pace in complex systems is understanding gained per unit of time, not action taken per unit of stress. It requires leadership that rewards stable comprehension over reactive movement, and a culture that prizes the durability of insight more than the spectacle of urgency.
To right-fit decision-making is to respect the system’s timescale, the team’s stressors, and one’s own psychology. It is not moderation for its own sake; it is design for generalization. Mycelium offers its own metaphor: growth that advances in pulses, continuously testing edges against their environment, integrating each local truth into a larger, coherent form. The practice of right-fitting is the same, a living form of cross-validation, a method of learning that moves, breathes, and remembers.
References
Malenko, A., Nanda, R., Rhodes-Kropf, M., & Sundaresan, S. (2023). Catching outliers: Committee voting and the limits of consensus when financing innovation (Working Paper No. 21-131). Harvard Business School. https://www.hbs.edu/faculty/Pages/item.aspx?num=62911
Barnett, Carole & Pratt, Michael. (2000). From Threat-Rigidity to Flexibility: Toward a Learning Model of Autogenic Crisis in Organizations. Journal of Organizational Change Management. 13. 74-88. 10.1108/09534810010310258.