Three Under-Explored Mechanisms in the Evolution of Behavioral Flexibility

Fellow Traveler
Oct 3, 2025
18 min read

Updated: Oct 20, 2025

ABSTRACT

The evolution of behavioral flexibility is typically modeled using niche breadth theory, reaction norms, optimal foraging, and bet-hedging. These frameworks capture much of the variance in when plasticity evolves, yet three mechanisms remain under-formalized.

H1 (Tail structure): Standard plasticity models parameterize environmental variability by mean, variance, and autocorrelation; they rarely treat extreme events as having independent structure. We formalize a differential prediction: when variance is matched but tail heaviness differs, selection for switching ability S is stronger in fat-tailed environments, especially for narrow-tolerance species.

H2 (Sensing dynamics): Reaction-norm slope captures response magnitude but not cue detection quality or response latency. We propose a testable decomposition in which accuracy and latency enter fitness independently of slope under short decision windows or serial correlation.

H3 (Return-phase hysteresis): In coupled consumer–resource systems, the ramp rate of returning to high exploitation after shocks changes collapse probability independently of mean extraction. We derive distinct predictions from standard threshold/bang-bang or marginal value rules.

Each hypothesis includes operational definitions, concrete experimental designs, and quantitative falsifiers. Our goal is not to replace existing theory but to specify conditions where these mechanisms add predictive power and to pre-commit to tests that could reject them. These mechanisms also illuminate a broader principle: optimal behavioral strategies are environment-specific, not universal — a logic that extends to epistemic strategies in decision-making and belief updating. We discuss implications for normative frameworks that prescribe context-independent updating rules.

1. INTRODUCTION

1.1 The Standard Toolkit

Organisms cope with environmental variability via three broad strategies:

(i) broad physiological tolerance (generalism),

(ii) phenotypic plasticity (state- or cue-dependent switching), and

(iii) bet-hedging (risk-spreading across time or offspring).

Canonical theories provide powerful guidance: niche breadth relates to range size, reaction norms relate phenotypes to environments, optimal foraging formalizes patch decisions, and bet-hedging makes variance–mean trade-offs explicit (e.g., Slatyer et al. 2013; Charnov 1976; Simons 2011; Starrfelt & Kokko 2012).

Still, mismatches persist between model predictions and field outcomes, especially under extremes, information constraints, and resource collapse dynamics.

1.2 Three Recognized Gaps

Gap 1: Extremes as independent structure. Much plasticity theory treats environmental variation as continuous fluctuations summarized by low-order moments; extremes are not given special theoretical status. Chevin & Hoffmann (2017) note that “the infrequent occurrence of extreme environments… means that they exert little direct selection on reaction norms” in current models, which rarely parameterize tail heaviness separately from variance.

Gap 2: Temporal dynamics under-modeled. Recent synthesis argues that the temporal dynamics of plasticity — how quickly and accurately organisms sense and respond — are under-modeled relative to static reaction-norm slope. Dupont et al. (2024) explicitly highlight this as a frontier (“beyond reaction norms”).

Gap 3: Return-phase ramp rates not parameterized. Resource systems exhibit hysteresis and collapse under over-exploitation; yet the return-phase ramp rate after recovery (fast vs guarded return) is not a standard parameter in classic foraging or threshold-harvesting models, which emphasize marginal gains or bang-bang controls (Charnov 1976; standard bioeconomic control).

1.3 Our Contribution

We articulate three testable, falsifiable hypotheses targeting these gaps. Each provides a differential prediction relative to standard models, a minimal mathematical sketch, operational measurement plans, and effect-size thresholds for falsification. We explicitly do not claim generality across all taxa or environments; rather, we specify boundary conditions where these mechanisms should matter. We also emphasize complementarity: these mechanisms are additions, not replacements, to established theory.

H1 (Tail structure): Extreme-value index alters selection on S at matched variance.

H2 (Sensing dynamics): Cue detection accuracy and response latency alter fitness independently of reaction-norm slope under specific temporal regimes.

H3 (Return-phase hysteresis): Return-phase ramp rate modulates collapse risk in consumer–resource systems under shocks, independent of mean extraction.

1.4 Broader Implications: Epistemic Parallels

While our hypotheses target behavioral flexibility in non-human organisms, the mechanisms we formalize have broader implications. The insight that “optimal strategy depends on environmental structure” applies equally to epistemic strategies — how organisms (including humans) should update beliefs under uncertainty.

Decision theory and Bayesian epistemology often prescribe universal updating rules: maximize expected utility, update beliefs proportionally to evidence strength, maintain calibrated confidence. Yet our framework suggests such prescriptions are incomplete without specifying environmental context. Just as H1 shows that tail structure alters selection on behavioral switching, it implies that belief-updating strategies should differ in fat-tailed versus thin-tailed information environments.

We return to these epistemic parallels in Section 4 (Discussion), after presenting the empirical mechanisms. Our primary goal remains falsifiable prediction in behavioral ecology; the philosophical implications emerge naturally from the formal structure of our hypotheses rather than being imposed externally.

2. THREE HYPOTHESES

2.1 HYPOTHESIS 1: Tail Structure Affects Plasticity Selection Independently of Variance

2.1.1 Literature Context

Standard assumption. Reaction-norm and plasticity models typically summarize environmental variability with mean, variance, and autocorrelation; extremes are not given distinct status in selection on plasticity. Chevin & Hoffmann (2017): “Phenotypic plasticity… may allow species to counter the detrimental effects of extreme conditions, but the infrequent occurrence of extreme environments… means that they exert little direct selection on reaction norms,” highlighting a gap in explicit treatment of extremes.

Gap. Extreme Value Theory (EVT) provides formal tail indices (e.g., generalized Pareto tail index ξ) to quantify heaviness beyond variance and autocorrelation (Coles 2001; Hill 1975). Plasticity theory seldom parameterizes ξ separately. Thus two environments with matched variance but different ξ would be indistinguishable in standard models but not under our hypothesis.

2.1.2 Differential Prediction

Standard prediction: With matched variance σ² and autocorrelation ρ, selection on switching ability S is similar across environments.

H1 prediction: If tail indices differ (ξ₂ > ξ₁), selection favors higher S in the fat-tailed environment, particularly when physiological tolerance T is narrow. Variance-only models cannot distinguish these regimes.

2.1.3 Formal Sketch

Let environment E be a stochastic process; define a regime-exit event when E exceeds tolerance bounds. Consider two synthetic inflow sequences for a chemostat:

Thin-tailed: Gaussian innovations with mean 0, variance σ² = 10, tail index (EVT) ξ₁ ≈ 0.
Fat-tailed: Peaks-over-threshold with GPD tail index ξ = 0.5 and calibrated scale so overall variance remains σ² = 10.

Standard models: f(σ², ρ) → identical selection on S.H1: f(σ², ρ, ξ) → stronger selection for higher S in fat-tailed case (greater hazard of boundary-crossing bursts despite matched variance).

We use ξ as the generalized Pareto tail index from EVT (estimated via peaks-over-threshold), not the standard Pareto shape parameter.

2.1.4 Empirical Test

Mesocosm experiment (Daphnia/algae). Program inflows with matched μ and σ² but different ξ (Gaussian vs GPD innovations), confirm via EVT diagnostics (Hill estimator, QQ plots). Measure S as:

Detection accuracy: ROC/AUC of changes in filtering rate in response to kairomones that signal low algal density.
Latency: time (min) from cue onset to a pre-registered threshold change in filtering rate.
Policy gain: long-run growth rate improvement vs a greedy baseline that ignores the cue.

Prediction: Fitness advantage of high-S genotypes is larger in fat-tailed treatment.

Falsifier: <10% difference in advantage (CIs overlapping 0) between tail treatments. EVT estimation requires >100–200 independent exceedances for stable ξ; this limits systems to those with ample observations.

Comparative test. Combine species-level tolerance estimates and behavioral innovation proxies with foraging-scale environmental tail metrics; test whether an S × ξ interaction improves range or persistence predictions beyond S × σ².

2.1.5 Falsification Criteria

H1 is rejected if (i) matched-variance, different-tail treatments yield no greater selection on S (effect size <10%, CI overlaps 0), (ii) comparative models show no predictive gain from ξ, or (iii) tail-heaviness does not amplify S benefits in narrow-tolerance taxa.

2.1.6 Scope Conditions

H1 strongest when: Narrow-tolerance species, environments where tail events occur within lifespans, regenerating resources with recovery intervals.

H1 weakest when: Cues are near-perfect, extremes are outside organismal timescales, or variance already predicts outcomes well.

2.1.7 Epistemic Parallel: Information Environments with Fat Tails

The logic of H1 extends naturally to belief updating. Just as organisms face resource environments with different tail structures, decision-makers face information environments with different distributions of signal quality.

In some domains (stable scientific measurements, quality-controlled data), signals are approximately Gaussian — extreme deviations from truth are rare. In others (social media, geopolitical forecasting, novel technologies), signals exhibit fat tails — extreme misinformation or radical shifts occur more often than variance predicts.

H1 implies that optimal epistemic strategies should differ across these environments. In fat-tailed information spaces, agents should maintain greater “belief flexibility” (analogous to behavioral S) — readiness to update rapidly in response to signals that would be dismissed as outliers in thin-tailed contexts. Conversely, strategies optimized for Gaussian information environments (e.g., Bayesian updating with narrow priors) may fail catastrophically when imported to fat-tailed domains.

Standard normative epistemology rarely parameterizes the tail structure of information environments. Our framework suggests this is a consequential omission.

2.2 HYPOTHESIS 2: Detection Quality and Latency Affect

Fitness Independently of Reaction-Norm Slope

2.2.1 Literature Context

Standard assumption. Plasticity is often represented by a reaction-norm slope relating phenotype to environment, with generic costs or lags handled implicitly. This collapses sensing and actuation into a single parameter.

Gap. A recent synthesis argues that temporal dynamics of plasticity — how quickly responses unfold and how information is acquired — remain under-modeled relative to static reaction norms (Dupont et al., “Beyond reaction norms,” TREE 2024).

Likewise, classic state-dependent/foraging models emphasize value and state but generally do not parameterize cue detection accuracy and response latency as distinct fitness components alongside slope. Moreover, a broad literature documents speed–accuracy trade-offs in animal decision-making, implying accuracy and latency are consequential and sometimes inversely related (Chittka et al., 2009).

2.2.2 Differential Prediction

Standard prediction: Two lineages with equal reaction-norm slope should perform similarly under the same variability regime.

H2 prediction: Holding slope constant, differences in accuracy (ROC/AUC) and latency produce independent fitness differences when decision windows are short or environmental series are positively autocorrelated.

2.2.3 Formal Sketch

We treat “switching ability” as a testable, heuristic composite (to be empirically compared with alternatives):

$$S = \text{slope}^\alpha \times \text{accuracy}^\beta \times \left(\frac{1}{\text{latency}}\right)^\gamma$$

with α, β, γ ≥ 0 estimated from data.

This multiplicative (log-additive) form is not asserted as truth; it encodes the hypothesis that accuracy and latency contribute to fitness beyond slope. Competing forms (additive, min-bottleneck) will be preregistered and compared via out-of-sample predictive performance.

2.2.4 Empirical Test: Zebrafish Visual Foraging

Task and manipulations. Adult zebrafish choose between “food-present” vs “absent” visual scenes. Cue validity is manipulated to 50% vs 90% (sham vs reliable). Decision windows: short (3–5 s) vs long (15–20 s). We measure:

Slope: change in foraging effort across a calibrated gradient of true food densities.
Accuracy: ROC/AUC for “scarcity detection” (correctly withholding effort when cue implies low payoff). Analyze with pROC and DeLong’s test; power targeting AUC differences ≥0.08 with ~200 trials per fish.
Latency: median response time post-cue crossing a pre-registered threshold.

Creating matched-slope groups with divergent accuracy/latency. (Crucial step.) We (1) screen a large initial pool, (2) estimate each fish’s slope from calibration blocks, (3) select individuals with slopes within ±10% but naturally divergent accuracy (ΔAUC > 0.15) and latency (Δlatency > 5 s).

If natural variation is insufficient, we use training protocols to improve cue discrimination (accuracy) without altering the mapping from true density to effort (slope), verified in holdout calibration. This yields groups with equal slope but different accuracy/latency.

Predictions. In short-window and positively autocorrelated regimes, high-accuracy/low-latency fish achieve higher long-run intake than equal-slope fish with poorer sensing, even after penalizing energetic costs of faster responses.

Falsifier: Fitness differences <10% or CIs overlapping 0 across matched-slope groups in the specified regimes; or slope alone explains outcomes.

2.2.5 Additional Falsification Checks

Vary reliability from 50→95% to test whether accuracy matters only when cues are imperfect.
Vary window length: prediction attenuates or disappears in long windows.
Compare multiplicative vs additive vs bottleneck functional forms via preregistered model comparison.

2.2.6 Scope Conditions

H2 strongest when: (i) Decision windows are short (seconds–minutes), (ii) cues are noisy (<95% reliable), (iii) environments show positive autocorrelation, (iv) actuation costs for rapid responses are not prohibitive.

H2 should not add value when: Windows are long (>1 h), cues are near-perfect (>95% reliability), or responses are constitutive (no discrimination required).

Speed–accuracy coupling is expected (Chittka et al., 2009); we therefore jointly model accuracy and latency rather than assuming independence.

2.2.7 Epistemic Parallel: Belief Updating Under Time Constraints

H2’s decomposition of plasticity into detection quality and response latency has direct parallels in human decision-making under time pressure.

Standard Bayesian epistemology focuses on what agents should believe given evidence, assuming unlimited processing time. But real decision-makers face temporal constraints — investment decisions have deadlines, medical diagnoses require speed, crisis response can’t wait for certainty.

H2 implies that normative frameworks should explicitly trade off three dimensions: belief magnitude (how much to update, analogous to slope), detection accuracy (recognizing when evidence genuinely requires updating), and response latency (how quickly updating occurs after evidence arrives).

In short-window environments — emergency response, high-frequency trading, real-time clinical decisions — latency may dominate. Acting on 70% confidence immediately can outperform waiting for 95% confidence. Yet normative epistemology rarely formalizes this trade-off; accuracy is treated as paramount, and speed as incidental.

Our framework suggests optimal epistemic strategies are environment-specific: short windows favor fast-approximate updating, long windows permit slow-accurate deliberation. There is no universal “best” updating rule.

2.3 HYPOTHESIS 3: Return-Phase Hysteresis Affects

Collapse Risk in Coupled Forager–Resource Systems

2.3.1 Literature Context

Standard foundations. The Marginal Value Theorem (MVT) sets a patch-leaving rule based on marginal gains (Charnov 1976). Renewable-resource control often yields threshold or bang-bang policies under constraints (e.g., minimum biomass, fixed costs), emphasizing levels rather than ramp rates. Ecosystem hysteresis and critical transitions are well-documented, but the organism-level return ramp after a shock is not typically parameterized as a distinct collapse-risk factor.

Gap. We find no model that holds long-run extraction constant while explicitly varying the return-phase ramp rate to test whether faster return increases the probability of crossing a depensation threshold (Allee effect), conditional on shock sequences (Dennis 1989).

2.3.2 Differential Prediction

Standard prediction: After recovery, return timing is chosen for efficiency; collapse risk is governed by thresholds and mean exploitation.

H3 prediction: Under regenerating resources and fat-tailed shocks, a fast return policy (small τ) increases collapse probability relative to a guarded return (large τ), even when average extraction is matched.

2.3.3 Formal Sketch

Resource X follows logistic growth with depensation:

$$\frac{dX}{dt} = rX\left(1 — \frac{X}{K}\right)\left(\frac{X}{X_c} — 1\right) — h(t)$$

where X_c is a viability threshold (Dennis 1989).

Policy h(t) ramps from low to high at rate determined by τ once a recovery threshold H_on is crossed; shocks subtract stock stochastically. H3 predicts Pr(X_t < X_c) increases as τ ↓ under heavy-tailed shocks.

2.3.4 Empirical Test

Chemostat microcosm (algae–rotifer). Control a consumer-removal pump by turbidity feedback, implementing fast vs guarded return ramps (τ = 0.5 h vs 6 h) after X crosses H_on.

Determine X_c in preliminary runs by depressing X until recovery fails, establishing the empirical threshold. Compare collapse frequencies across τ treatments, holding long-run mean removal equal by adjusting duty cycles.

Agent-based simulations. Implement identical mean extraction, vary τ, inject matched-variance shocks with different tail indices (linking H1 & H3). Estimate collapse probability surfaces over (τ, ξ).

2.3.5 Falsification Criteria

H3 is rejected if (i) collapse rates do not differ between τ treatments (Δ<10%, CI overlaps 0), (ii) fast return never increases collapse probability under tested shock regimes, or (iii) results reduce to standard threshold predictions without ramp-rate dependence.

2.3.6 Scope Conditions

H3 matters when: (i) Resources exhibit depensation (Allee effects), (ii) shocks are bursty/fat-tailed, (iii) exploitation can overshoot during recovery.

H3 should not matter when: Depensation is absent or shocks are mild.

2.3.7 Epistemic Parallel: Confidence Restoration After Forecast Failures

H3’s distinction between mean extraction and ramp rate has an intriguing epistemic analog: how forecasters restore confidence after prediction failures.

After major forecast errors — missed financial crashes, failed political predictions, incorrect scientific estimates — decision-makers face a dilemma analogous to consumer-resource return dynamics. They can quickly restore confidence to pre-failure levels (fast return, small τ) or gradually rebuild trust in their models (guarded return, large τ).

Fast confidence restoration maximizes short-term decision-making efficiency but risks compounding errors if underlying models remain flawed (analogous to overshooting carrying capacity). Guarded restoration sacrifices immediate decisiveness for long-term robustness.

Research on expert judgment shows systematic patterns: overconfident forecasters tend toward fast confidence restoration, well-calibrated forecasters toward guarded return. Yet normative frameworks rarely specify how quickly confidence should be restored after failures — only whether updating should occur.

H3 suggests this rate parameter (epistemic τ) may independently affect long-run forecast quality, even holding mean confidence levels constant.

3. INTEGRATION AND SCOPE

3.1 Complementarity with Existing Theory

These hypotheses add knobs to established models.

H1 introduces a tail index ξ to distinguish matched-variance environments;

H2 separates sensing (accuracy, latency) from static slope;

H3 adds return ramp τ to consumer–resource dynamics.

None replace the basic insights of niche breadth, reaction norms, MVT, or bet-hedging. They specify conditions where additional parameters should measurably improve predictions.

3.2 Empirical Prioritization and Feasibility

H1 is strong conceptually and experimentally tractable with synthetic inflows and EVT diagnostics, though tail-index estimation requires many observations (EVT methods: Hill estimator; GPD peaks-over-threshold).

H2 is experimentally tractable but comparatively data-poor across species; early tests should use lab systems (fish, insects) with standardized tasks; building a comparative database will take coordinated effort.

H3 is tractable both in chemostats and ABMs; existing literature on rotifer–algae chemostats supports controlled stock manipulation and feedback-based extraction.

3.3 When Added Complexity is Justified

We do not advocate routine inclusion of ξ, accuracy/latency, or τ in every model. Complexity is warranted when:

(i) extremes occur within lifetimes (H1),

(ii) decisions are time-constrained or cues are imperfect (H2), and

(iii) resources show depensation and shock-recovery sequences (H3). Otherwise, standard models remain preferred for parsimony.

3.4 Predictive and Management Relevance

H1 suggests risk assessment should incorporate tail metrics at behavioral scales (patch variability, not just climate means).

H2 suggests management and conservation interventions could target information environments (improving cue reliability) as much as resource levels.

H3 suggests harvest policies should consider ramp protocols after closures/recoveries, not solely mean quotas.

3.5 Epistemic Synthesis: Context-Specificity as Fundamental Principle

Across all three hypotheses, a common theme emerges: optimal strategies are environment-specific, not universal. Whether behavioral (H1-H3) or epistemic (belief updating), effective strategies must be matched to environmental structure.

This has implications for normative frameworks in decision theory and epistemology. Prescriptions like “maximize expected utility,” “update proportionally to evidence,” or “maintain calibrated confidence” are incomplete without specifying:

Tail structure of uncertainty (ξ): How often do extreme events occur?
Temporal dynamics: How quickly must decisions be made?
Penalty landscapes: What are the costs of overshoot vs undershoot?

Our behavioral ecology framework formalizes these contextual parameters (ξ, decision window length, depensation thresholds). The same parameters should appear in normative epistemology.

We develop this argument further in Section 4.

3.6 Failure is Informative

Each hypothesis is falsifiable by small effect sizes or absence of predicted interactions. If tests fail, we gain boundary conditions for when classical models suffice.

4. DISCUSSION

4.1 Summary of Contributions

We identify three narrow, testable mechanisms that may improve predictions for the evolution and performance of behavioral flexibility: tail structure (H1), sensing dynamics (H2), and return-phase hysteresis (H3). Each is grounded in recognized gaps:

underspecified extremes in plasticity theory (H1), limited treatment of temporal sensing dynamics beyond slope (H2), and omission of return ramp as a collapse-risk factor (H3). Our formal sketches are deliberately minimal; our empirical designs specify operational metrics, experimental configurations, and quantitative falsifiers.

We emphasize conditionality. These mechanisms should matter most for narrow-tolerance species in bursty environments (H1), organisms facing short decision windows and imperfect cues (H2), and coupled consumer–resource systems with depensation and shocks (H3). Where those conditions do not hold, existing models remain adequate and more parsimonious.

4.2 The Path Forward

The path forward is straightforward: run the experiments and analyze the data. If the differential predictions fail, that is a positive result for theory — tightening its scope. If they succeed, we will have identified specific domains where adding ξ, accuracy/latency, or τ substantively improves prediction and management.

Either way, we move from broad verbal claims to refutable mechanisms and measurable parameters, aligning the study of behavioral flexibility with the empirical rigor demanded by contemporary ecology and evolution.

4.3 Philosophical Implications: Beyond Behavioral Ecology

4.3.1 The Behavioral-Epistemic Parallel

Our hypotheses target behavioral flexibility in non-human organisms, but the formal structure applies equally to epistemic strategies — how any organism, including humans, should update beliefs under uncertainty.

Consider the parallel:

Behavioral plasticity: Switching between phenotypes (foraging modes, activity levels) in response to environmental cues
Epistemic plasticity: Updating beliefs (confidence levels, probability estimates) in response to evidence

Both involve:

Prior state (current phenotype / current belief)
Environmental signal (resource level / new evidence)
Detection & response (cue processing / evidence evaluation)
Updated state (new phenotype / new belief)
Fitness consequences (survival/reproduction / decision quality)

Standard reaction-norm theory models (1)-(4) but often treats (5) as straightforward optimization. Our framework shows that optimal strategies at steps (2)-(3) depend critically on environmental structure — and the same holds for epistemic updating.

4.3.2 Limitations of Context-Free Normative Principles

Much normative epistemology and decision theory prescribes context-free updating rules:

Bayesian updating: “Update beliefs proportionally to evidence strength” (no specification of latency or accuracy thresholds)
Expected utility maximization: “Choose action with highest expected value” (no specification of decision window or tail structure)
Calibration: “Maintain confidence levels that match long-run accuracy” (no specification of return-phase dynamics after failures)

Our framework suggests these prescriptions are incomplete rather than wrong. They’re analogous to saying “organisms should adapt to their environments” without specifying:

Which aspects of environment (mean, variance, or tails)?
How quickly (latency vs accuracy trade-offs)?
With what risk tolerance (collapse thresholds)?

Just as behavioral ecologists now recognize that optimal plasticity depends on niche breadth, autocorrelation, and information reliability, epistemologists should recognize that optimal updating depends on tail structure, decision windows, and error costs.

4.3.3 Toward Environment-Parameterized Epistemology

What would environment-parameterized normative epistemology look like? It would specify optimal updating strategies as functions of environmental parameters:

For H1 (tail structure):

Thin-tailed information environments (laboratory science, quality-controlled data): Narrow confidence intervals, reject extreme claims strongly
Fat-tailed information environments (geopolitics, pandemic forecasting): Wider confidence intervals, maintain greater belief flexibility

For H2 (temporal dynamics):

Long decision windows (academic research, policy planning): Prioritize accuracy over speed
Short decision windows (emergency response, trading): Prioritize speed over accuracy, act on partial information

For H3 (return dynamics):

High collapse risk (nuclear safety, financial systemic risk): Guarded confidence restoration after failures
Low collapse risk (consumer product design, marketing): Faster confidence restoration acceptable

These aren’t arbitrary choices — they’re environment-specific optima, just as behavioral strategies are in H1-H3.

4.3.4 Implications for Cognitive Science and AI

These insights have direct implications for:

Human decision support systems: Should adapt updating parameters (confidence thresholds, response latencies, restoration rates) to environmental structure, not impose universal rules.

AI forecasting systems: Current Bayesian and ML models rarely parameterize tail structure (ξ) or explicitly trade off accuracy vs latency. Our framework suggests these should be tunable hyperparameters matched to deployment context.

Organizational learning: After forecast failures, organizations face return-phase dilemmas (H3 epistemic parallel). Optimal confidence restoration rates should depend on collapse risk, not universal rules about “learning from mistakes.”

Future work should test whether epistemic predictions from H1-H3 actually improve human/AI decision quality in diverse environments.

4.4 Caveats and Boundaries

We emphasize that the epistemic parallels discussed in Section 4.3 are speculative extensions, not empirical claims of this paper. Our primary contribution remains the three falsifiable hypotheses in behavioral ecology (H1-H3).

The philosophical implications emerge from the formal structure of our models — the same mathematics that describes behavioral switching can describe belief updating. Whether these parallels reflect deep principles or superficial analogies remains to be determined through empirical work in decision science and cognitive psychology.

We present these connections to:

Motivate future research bridging behavioral ecology and decision science
Show how empirical findings in one domain can inform normative frameworks in another
Identify testable predictions about human/AI updating strategies

But we do not claim to have proven that epistemology should be environment-parameterized — only that our framework makes this possibility worth investigating.

5. CONCLUSION

Underspecified extremes in plasticity theory (H1),

limited treatment of temporal sensing dynamics beyond slope (H2),

and omission of return ramp as a collapse-risk factor (H3).

Our formal sketches are deliberately minimal; our empirical designs specify operational metrics, experimental configurations, and quantitative falsifiers. We emphasize conditionality: these mechanisms should matter most for narrow-tolerance species in bursty environments (H1), organisms facing short decision windows and imperfect cues (H2), and coupled consumer–resource systems with depensation and shocks (H3).

Where those conditions do not hold, existing models remain adequate and more parsimonious.

Broader Principle: Context-Specificity Over Universality

These mechanisms illuminate a principle that extends beyond behavioral ecology:

optimal strategies are environment-specific, not universal. Whether behavioral or epistemic, effective strategies must match environmental structure — tail heaviness, time pressure, penalty landscapes.

This challenges normative frameworks that prescribe universal updating rules without environmental context. But it also suggests a research program: empirically determine which updating strategies succeed in which environments, for organisms from bacteria to humans to AI systems.

Our three hypotheses provide starting points. If they survive testing, they’ll have identified specific environmental parameters (ξ, decision window length, depensation thresholds) that should appear not only in behavioral ecology but in decision theory, epistemology, and machine learning.

The path forward remains empirical: run the experiments, measure the parameters, test the predictions. The philosophical implications will follow from the data — or be refuted by it.

Word count: ~5,230 words

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FIGURES (Placeholders)

Figure 1 (H1 schematic): Two synthetic inflow series with matched variance but different tail indices (ξ); prediction overlay for S selection strength.

Figure 2 (H2 decomposition): Conceptual diagram contrasting equal slopes with different accuracy/latency; short vs long decision windows.

Figure 3 (H3 dynamics): Time series of X(t) under fast vs guarded return ramps; collapse probability vs τ across shock tail indices.