54 experiments across 9 agent-based models exploring Beinhocker's Origin of Wealth
Last updated March 27, 2026 · Seed 42 · Full reproducibility
Eric Beinhocker argues that the economy is not an equilibrium machine but a complex adaptive system — an evolving ecology of strategies, technologies, and institutions that perpetually generates novelty, never settles into rest, and produces its own crises as naturally as it produces growth. This research program tests that thesis through computational simulation: 9 models, 54 experiments, and thousands of agent interactions producing measurable emergence, non-linearity, power laws, and evolutionary dynamics.
Launch Simulations →Nine models, fifty-four experiments. Each section presents key findings, metrics, and emergent behaviors.
Heterogeneous adaptive traders with evolving forecasting rules on a call market. Based on Arthur, Holland, LeBaron, Palmer & Tayler (1997).
Learning agents produce fat-tailed returns with a Hill tail index of 2.55 — strikingly close to the empirical cubic power law (~3) found in real equity markets — while the rational baseline produces near-Gaussian tails. Fat tails, volatility clustering, and excess volume are the natural signature of adaptive agents co-evolving in a market.
As soon as heterogeneity and learning are introduced, things get much richer and more complex. … All this price movement is driven by the dynamic interactions of various rules in the population and has little or nothing to do with changes in the underlying economic value of the stock. Nor are the complex patterns due merely to random noise. Instead, there is a complex battle of beliefs going on within the heads of agents and among the agents, which leads to volatility and complex patterns in the market.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 6, p. 138| Experiment | Key Parameter | Kurtosis | Tail Index | Vol. Cluster | Insight |
|---|---|---|---|---|---|
| 1a. Learning (baseline) | Default (25 agents) | 45.3 | 2.55 | 0.489 | Realistic market statistics emerge from learning |
| 1b. Rational (no learning) | Fixed rules | 4.8 | 46.38 | -0.055 | Equilibrium fails: highest volatility, near-zero volume |
| 2. High Mutation | Rate 0.10 (3x) | 51.7 | 2.50 | 0.555 | More mutation sustains edge-of-chaos dynamics |
| 3. Large Population | 100 agents (4x) | 18.0 | 3.85 | 0.232 | Diversity stabilizes; fat tails persist |
| 4. Low Risk Aversion | Lambda 0.1 (5x lower) | 69.5 | 2.75 | 0.410 | Proto-bubbles; skewness 2.78 |
| 5. Fast Evolution | GA interval 50 (5x faster) | 140.9 | 2.79 | 0.449 | Red Queen dynamics triple kurtosis |
The rational-expectations baseline — the gold standard of neoclassical finance — produces the worst outcomes: highest volatility, largest mispricing, near-zero liquidity. The perpetually out-of-equilibrium learning market tracks fundamentals better, trades more actively, and distributes wealth more equally. Equilibrium is not a useful approximation; it is the opposite of what happens. Adaptation and instability are two sides of the same evolutionary coin.
Four-echelon supply chain with Sterman's anchor-and-adjust ordering heuristic. Based on Forrester (1961) and Sterman (1989).
Shipping delay dominates all other factors with super-exponential cost scaling: delay 2 costs $3,236; delay 4 costs $35,908 (11x); delay 5 costs $142,427 (44x). A single, tiny, one-time demand perturbation generates 30–50 ticks of wild endogenous oscillation — the business cycle is purely structural, not driven by external shocks.
The Beer Game is not a mere wiggling-jelly propagation mechanism. The game receives a single exogenous shock — the increase in orders from four to eight. But unlike a jelly given a single tap, once the oscillations start in the Beer Game, the system never returns to equilibrium. The ultimate source of the oscillations in the Beer Game is not the exogenous shock itself (it just gets things started), but the behavior of the participants and the feedback structure of the system. The system is not propagating exogenous dynamics; it is endogenously creating them.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 8, pp. 171–172| Experiment | Key Parameter | Total Cost | Max Bullwhip | Insight |
|---|---|---|---|---|
| 1. Baseline (behavioral) | Delay=2, step demand | $3,236 | 7.89x | Severe bullwhip from simple heuristics |
| 2. Rational comparison | Optimal ordering | $1,984 | 2.36x | 63% bounded-rationality surcharge |
| 3. Longer delay (4) | Delay doubled | $35,908 | 29.18x | 11x cost; super-linear scaling |
| 4. Sine demand | Continuously varying | $40,211 | 7.59x | Never converges; perpetual oscillation |
| 5. Information sharing | Demand transparency | $1,844 | 5.05x | 43% savings; nearly matches rational |
| 6. Extreme delay (5) | Delay=5, 80 ticks | $142,427 | 60.40x | Signal destruction; 2,137 cases idle |
Business cycles may be endogenous rather than exogenous. The architecture of the system — its delays, topology, information structure — dominates agent sophistication by an order of magnitude. The structure tax (44x from delay) dwarfs the human tax (1.6x from bounded rationality). Economies are not machines to be fine-tuned but weather systems that generate their own dynamics.
Kauffman random Boolean networks exploring the phase transition between order and chaos as a function of connectivity. Based on Kauffman (1969, 1993).
At K=2, the network sits at the edge of chaos with measured Derrida parameter 1.010 (theoretical: 1.000). This is the sweet spot where perturbations propagate far enough to enable adaptation but not so far as to destroy coherence. At K=5, cascades engulf 93.4% of the network and no stable states can be found — the complexity catastrophe.
This kind of interdependency in a network creates what Kauffman calls a complexity catastrophe. The effect occurs because as the network grows, and the number of interdependencies grows, the probability that a positive change in one part of the network will lead to a cascade resulting in a negative change somewhere else grows exponentially with the number of nodes. This in turn means that densely connected networks become less adaptable as they grow.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 7, p. 152| Experiment | Regime | Derrida | Cascade % | Attractors | Insight |
|---|---|---|---|---|---|
| 1. Phase Diagram | Sweep K=1..7 | varies | varies | varies | Sharp boundary follows 2Kp(1-p)=1 |
| 2. Ordered (K=1) | Ordered | 0.445 | 5.8% | 30/30 | Perturbations absorbed; stable but rigid |
| 3. Critical (K=2) | Critical | 1.010 | 11.8% | 30/30 | Edge of chaos; max cascade 24 (48%) |
| 4. Chaotic (K=5) | Chaotic | 2.265 | 93.4% | 0/30 | Total chaos; no attractors found |
| 5. Hierarchy vs Random | Chaotic | ~1.50 | 60-62% | 30/30 | Hierarchy's value emerges at larger N |
| 6. Scale (N=20..200) | Critical | ~1.00 | 7-23% | 30/30 | Critical networks scale gracefully |
The ~7-person working group limit is not arbitrary cognitive psychology but an emergent constraint from the mathematics of interdependent decisions. Hierarchy is computational infrastructure for managing complexity, not merely a power structure. Organizations should target K=2 effective connectivity — the edge of chaos where complex adaptive systems are most productive.
Hybrid Jain-Krishna / Bak-Sneppen model of ecosystem evolution with directed weighted interaction graph. Self-organized criticality and power-law cascades.
Cascade sizes follow a power law with exponent ~2.0, consistent with empirical extinction data. The system drives itself to criticality without tuning. Tripling connection density transforms the ecosystem from occasional small cascades (mean 2.5) to perpetual catastrophe (mean 56.9) — the connectivity-fragility paradox.
Jain and Krishna noted three distinct phases to the punctuated equilibrium pattern. First, in a random phase, the network percolates along without much structure, and random changes occur without much effect. Then, an innovation sends the network suddenly into a growth phase. … The network continues to bubble along for a while in the organized phase, but then an innovation or a random change hits a keystone species. Changes influencing the keystone species radiate into the structure, and the network crashes in a wave of extinction. The process then begins again with a new random phase.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 8, p. 174| Experiment | Key Parameter | Mean Cascade | Max Cascade | Insight |
|---|---|---|---|---|
| 1. Baseline (100 species) | Conn. prob. 0.05 | 2.5 | 21 | Power-law cascades; exponent ~2.0 |
| 2. Small ecosystem (20) | N=20 | 2.1 | 12 (60%) | Volatile; 10 punctuation events |
| 3. Large ecosystem (200) | N=200 | 23.4 | 120 (60%) | Rare but catastrophic tail events |
| 4. Dense (0.15) | Conn. prob. 3x | 56.9 | 89 | Perpetual catastrophe; bimodal cascades |
| 5. Sparse (0.02) | Conn. prob. 0.4x | 2.4 | 11 | Achieved ORGANIZED phase; low diversity |
| 6. Cascade test | Systematic removal | 2.2 | 9 | Keystone heuristic misses structural nodes |
Creative destruction is not an occasional disruption of an otherwise stable system — it is the system's natural mode of operation. The power-law cascade distribution explains why most innovations cause minor adjustments while a handful — the steam engine, the internet — trigger waves of creative destruction reshaping entire industries. Disequilibrium is the norm.
Harrington's organizational adaptation simulation with tournament-based promotion in changing environments. Based on Harrington (1999) and March (1991).
The promotion tournament is a rigidity ratchet: it ruthlessly selects rigids upward during stable periods, then delivers catastrophic performance collapse (+0.87 drop) when the environment shifts. At stability=500 (the "Thatcher trap"), the organization is 97.5% rigid with peak performance of 1.44 and zero resilience. At stability=20, the hierarchy inverts: the top is 100% flexible, and transition costs vanish.
Evolutionary systems work best when their sensitivity to change is in a medium, in-between range. If an evolutionary system is too insensitive to change, then the system will not be able to keep up with the pace of change in its environment. However, if a system is overly sensitive to change, then small changes can have large consequences. This oversensitivity is a problem because if a system has been successful in the past, then few major changes are likely to improve it. Rather, the odds are that the vast majority of possible major changes will harm it.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 7, pp. 157–158| Experiment | Stability | Overall Rigid% | Avg Perf. | Transition Cost | Insight |
|---|---|---|---|---|---|
| 1. Baseline | 100 | 75.0% | 1.194 | +0.337 | Purge-crash cycle |
| 2. High Stability | 500 | 97.5% | 1.439 | n/a | Thatcher trap: peak performance, zero resilience |
| 3. High Volatility | 20 | 60.0% | 1.044 | +0.041 | Top 100% flexible; hierarchy inverts |
| 4. Random Mode | 100 (rand) | 90.0% | 1.256 | +0.224 | Pattern matters as much as frequency |
| 5. Deep Hierarchy (6 levels) | 100 | 71.4% | 1.192 | +0.049 | Every level above L0 is 100% rigid by tick 50 |
| 6. Shallow Hierarchy (2 levels) | 100 | 75.0% | 1.096 | +0.138 | Less amplification, more stochastic noise |
| 7. High Experience Weight | 100 | 52.5% | 3.116 | +0.011 | Seniority overwhelms strategy; 0 tick recovery |
The key to long-term survival is not being the best adapted to the current environment, but being the most adaptable to environments that have not arrived yet. Pure exploitation yields peak 1.44 performance with zero resilience. Pure exploration yields steady 1.04 with perfect resilience. The 25% gap is the cost of insurance — and you cannot know if the premium is worth paying until the disruption arrives.
Iterated Prisoner's Dilemma with evolutionary dynamics on a spatial grid. Seven strategies compete: AllC, AllD, TFT, GTFT, Pavlov, Random, Grudger. Based on Axelrod (1984).
The same strategies, payoff matrix, and initial conditions produce diametrically opposite outcomes depending on population structure: spatial yields 100% cooperation (Grudger dominant at 46%), while the well-mixed tournament yields 0% cooperation (AllD dominance at 100%). Population structure is the foundation of cooperation.
That we are conditional cooperators and altruistic punishers should not be surprising. Our hominid ancestors spent about 2 million years of their existence living in small bands for which cooperative behavior and survival were highly correlated. Today, people still inhabit networks of social interactions in which reciprocity — I'll scratch your back if you scratch mine — is important. We are all better off if we help each other out, but this creates the potential for abuse by those who take benefits without giving back.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 6, p. 121| Experiment | Condition | Coop. Rate | Winner | Insight |
|---|---|---|---|---|
| 1. Spatial Baseline | 50x50 grid, noise=0 | 100% | Grudger (46%) | Spatial clustering enables cooperation |
| 2. Tournament Baseline | Well-mixed, 100 agents | 0% | AllD (100%) | Without spatial protection, defection wins |
| 3. High Noise (10%) | Spatial, noise=0.10 | 75.3% | Pavlov (97.6%) | Win-Stay/Lose-Shift corrects errors best |
| 4. Zero Noise | Spatial, noise=0 | 100% | Grudger (46%) | Permanent retaliation is optimal when deterministic |
| 5. High Mutation (5%) | Spatial, mut=0.05 | 97.3% | Grudger (71%) | All 7 strategies persist; cooperation robust |
| 6. High Temptation (T=10) | Spatial, T=10 | 99.9% | Grudger (94%) | Higher temptation strengthens cooperation (paradox) |
Cooperation is an emergent property of spatial evolutionary dynamics, not a pre-programmed outcome. Structure matters — the same agents with the same rules produce radically different macro-level outcomes depending on population structure. Markets, legal systems, and social norms evolve bottom-up from repeated strategic interactions.
Heterogeneous agents on a 2D resource landscape with vision, metabolism, movement, and optional reproduction. Based on Epstein & Axtell (1996).
Moderate inequality (Gini 0.26–0.40) emerges from identical behavioral rules and random initial conditions. Metabolism matters more than vision for survival (r = -0.55 to -0.83 vs r = 0.22 to 0.39). With reproduction enabled, traits evolve cumulatively toward near-optimal values: 97.3% of agents converge to metabolism=1 after 300 ticks.
The answer is, in essence, “everything.” The skewed distribution is an emergent property of the system. It is a macro behavior that emerges out of the collective micro behavior of the population of agents. The combination of the shape of the physical landscape, the genetic endowments of the agents, where they were born, the rules that they follow, the dynamics of their interactions with each other and with their environment, and, above all, luck all conspire to give the emergent result of a skewed wealth distribution.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 4, p. 86| Experiment | Key Parameter | Survival | Gini | Mean Wealth | Insight |
|---|---|---|---|---|---|
| 1. Baseline | 400 agents, 50x50 | 43% | 0.375 | 211.6 | Selection on metabolism > vision |
| 2. High Density (800) | Double population | 36% | 0.395 | 153.2 | Competition suppresses wealth |
| 3. Low Regrowth (0.25) | Quarter regrowth | 28% | 0.263 | 145.3 | Scarcity equalizes: not enough surplus |
| 4. Reproduction | Threshold=50, 300 ticks | 326% | 0.148 | 33.6 | Trait evolution; Malthusian equilibrium |
| 5. Large Grid (100x100) | 800 agents, big grid | 16% | 0.376 | 225.9 | Geography dominates genetics |
| 6. Extreme Ranges | Vision 1-10, met 1-6 | 33% | 0.308 | 247.8 | Brutal selection; dead invisible to Gini |
Wealth inequality is not imposed externally but emerges endogenously from agent heterogeneity interacting with resource geography. The reproduction experiment demonstrates how differential survival and heredity with variation produce directional evolutionary change — a purely economic analog of biological natural selection. The system never reaches static equilibrium.
Firms search a 12-dimensional binary fitness landscape with tunable epistatic interdependencies (K), using local hill-climbing, long-jump adaptation, and recombination. Based on Kauffman's NK model.
Landscape ruggedness (K) drives market turbulence: K=8 produces 40 firm destructions and 0.483 technology diversity, while K=1 produces only 17 destructions and 0.222 diversity. The mixed-strategy portfolio (local + long-jump + recombination) outperforms any single search strategy, confirming that diversity of approaches is the source of economic fitness.
For landscapes that are in between, are rough-correlated, and have complex features such as plateaus, holes, and portals, evolution is hard to beat. And when the landscape is constantly changing, when the search problem is a dynamic one, when one must balance the tension between exploring and exploiting — evolution truly is the grand champion.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 9, p. 213| Experiment | K | Strategy | Best Fitness | Destructions | Insight |
|---|---|---|---|---|---|
| 1. Baseline | 4 | Mixed | 0.800 | 31 | Local climbers dominate final population |
| 2. Smooth (K=1) | 1 | Mixed | 0.656 | 17 | Quick convergence, then stagnation |
| 3. Rugged (K=8) | 8 | Mixed | 0.767 | 40 | Highest innovation AND destruction |
| 4. High Mutation | 4 | Mixed, mut 3x | 0.800 | 32 | Noise is not a substitute for strategy diversity |
| 5. All Local Climbers | 4 | 100% local | 0.800 | 24 | Fast initial gains, then trapped on local optima |
| 6. All Long Jumpers | 4 | 100% long-jump | 0.800 | 23 | Slower convergence, sustained exploration |
Technology is fundamentally combinatorial search. No single search strategy dominates — economies benefit from a portfolio of approaches: some firms doing incremental improvement, others making radical leaps, others recombining. Creative destruction (Schumpeter's gale) is reproduced, with intensity depending on landscape ruggedness (industry complexity).
Four interacting sub-models — supply chain, stock market, ecosystem dynamics, and organizational adaptation — linked through composite firm health, dependency networks, and cascade failures.
Scale creates fragility, not just bigger failures: at 40 firms, the market crash kills 28 (70%); at 100 firms, the same supply shock triggers total wipeout (100%) before the panic even arrives. The cascade arrives 47 ticks earlier in the larger economy — a phase transition in network behavior. Shock sequencing matters more than magnitude: a 2.5x supply shock alone causes zero failures, but combined with a panic, it is lethal.
Oscillations that do not settle down, punctuated equilibrium, and power laws — these are all signature behaviors of a complex adaptive economy at work. The real-world economy is a far more interesting place than the equilibrium world imagined by Traditional Economics. Complexity Economics does not have all the answers to the puzzle of economic patterns, but it provides us with new tools to begin to understand how these various factors combine to result in the behaviors we observe.
— Eric D. Beinhocker, The Origin of Wealth, Chapter 8, p. 185| Experiment | Scenario | Mean GDP | Max Unemploy. | Cascades | Insight |
|---|---|---|---|---|---|
| 1. Normal Operations | No shocks, 200 ticks | 11.20 | 0% | 0 | Peacetime rigidity drift (flex 0.40 to 0.20) |
| 2. Supply Shock | 2.5x demand at t=100 | 15.42 | 0% | 0 | Bullwhip visible; GDP overshoots 3x |
| 3. Tech Disruption | Forced at t=150 | 11.20 | 0% | 0 | Zero GDP impact; flex jumps 0.20 to 0.64 |
| 4. Market Crash (40) | Triple shock sequence | 18.19 | 67.5% | 28, 3 | Panic is lethal; 10-tick delay before cascade |
| 5. Stress Test | Repeated shocks, 300t | 12.75 | 0% | 0 | Flexibility ratchets to 0.80; full survival |
| 6. Large Economy (100) | Market crash, 100 firms | 18.26 | 97% | 100, 3 | Total wipeout; phase transition in fragility |
Repeated, spaced shocks make the economy more resilient by driving organizational flexibility upward — validating Beinhocker's argument that economies need periodic disruption to maintain adaptive capacity. The integrated model shows how supply chain stress, market sentiment, technology shifts, and organizational rigidity interact to produce emergent macroeconomic dynamics that no single sub-model could generate alone.
Cross-cutting themes from the synthesis, each demonstrated by one or more simulations.
The rational-expectations baseline produces the worst market: highest volatility (0.0833), price drift, near-zero volume (0.05). Learning agents, perpetually out of equilibrium, track fundamentals more closely with half the volatility (0.0423) and 100x more trading volume. Fat tails (Hill index 2.55) and volatility clustering (0.489) are the natural signature of adaptive agents.
Shipping delay dominates all other factors with super-exponential cost scaling: delay 2 costs $3,236; delay 5 costs $142,427 (44x). A single one-time demand change generates 30+ ticks of wild endogenous oscillation. The structure tax (44x from delay) dwarfs the human tax (1.6x from bounded rationality). Business cycles are structural, not exogenous.
At K=2 (Derrida parameter 1.010), the system maximizes information processing. At K=5, cascades engulf 93.4% of the network. The complexity catastrophe is scale-dependent: K=4 works at N=20 but paralyzes at N=150. Hierarchy cuts cascades from 63% to 36% at scale — it is computational infrastructure for managing complexity.
Cascade sizes follow a power law with exponent ~2.0, consistent with empirical extinction data. The system drives itself to criticality without tuning. Dense networks (conn. prob 0.15) produce perpetual catastrophe with mean cascade 56.9 and bimodal distribution. The connectivity-fragility paradox: more connections means more productive and more fragile simultaneously.
Pure exploitation yields peak 1.44 performance during stability but catastrophic collapse at disruption. Pure exploration yields steady 1.04 with perfect resilience. The tournament is a rigidity ratchet that purges adaptive capacity during stable periods. The 25% performance-resilience gap is the cost of insurance against an uncertain future. Diversity is not inefficiency.
23 comparisons between simulation outputs and published empirical data. 14 strong, 8 moderate, 1 weak match.
| Model | Prediction | Real-World Evidence | Match |
|---|---|---|---|
| Stock Market | Fat tails (Hill exponent 2.55) | S&P 500 tail exponent 2.5–4.0 (Gopikrishnan et al. 1999, Cont 2001) | Strong |
| Volatility clustering (|return| AC = 0.49) | ARCH/GARCH effects in all equity markets (Engle 1982, Bollerslev 1986) | Strong | |
| Near-zero return autocorrelation | Real returns ~0 autocorrelation; simulation: -0.357 | Weak | |
| Volume-volatility correlation | Positive correlation documented universally (Karpoff 1987) | Moderate | |
| Endogenous wealth inequality (Gini 0.43) | US household wealth Gini ~0.85 (Wolff 2017); direction correct, magnitude lower | Moderate | |
| Excess kurtosis (42–211) | Empirical: 5–50 for daily returns (Cont 2001); simulation inflated | Moderate | |
| Beer Game | Bullwhip ratios 1.7–7.9x | Real supply chains: 1.0–8.0x (Lee et al. 1997, Cachon et al. 2007) | Strong |
| Super-linear cost scaling with delay | Convex lead-time cost documented (De Treville et al. 2004) | Strong | |
| Information sharing cuts cost 43% | VMI and POS sharing: 20–35% savings (Gavirneni et al. 1999) | Strong | |
| Bounded-rationality cost: 1.6–2.7x | Beer Game experiments: 2–3x median (Sterman 1989) | Strong | |
| Conservative ordering outperforms aggressive | Behavioral operations consensus (Schweitzer & Cachon 2000) | Strong | |
| Boolean Network | Phase transition at K=2, p=0.5 | Theoretical prediction confirmed; real gene networks K~2 (Aldana 2003) | Strong |
| Sharp phase boundary 2Kp(1-p)=1 | Derrida & Pomeau (1986) proof; computational confirmation | Strong | |
| Cascade bimodality at K=3 | Computational confirmation; limited direct empirical data | Moderate | |
| Hierarchy reduces cascades 30–40% | Qualitative support (Simon 1962, Perrow 1984) | Moderate | |
| Effective group size 5–9 | Hackman 2002, Dunbar 1992; extensive empirical support | Moderate | |
| Punctuated Eq. | Power-law cascade exponent ~1.8–1.9 | Fossil record extinctions 1.5–2.5 (Newman & Palmer 2003) | Strong |
| Dense networks are fragile | Financial network fragility (Haldane & May 2011) | Strong | |
| Large systems: rare but catastrophic events | Mass extinctions, financial crises, large-system outages | Strong | |
| Perpetual punctuated equilibrium cycles | Fossil record; organizational change (Gersick 1991) | Strong | |
| Rigids/Flex. | Tournament selection drives rigidity | Structural inertia theory (Hannan & Freeman 1984) | Strong |
| Optimal mix depends on volatility | Contingency theory (Burns & Stalker 1961) | Strong | |
| Exploration-exploitation tradeoff (25%) | March (1991); empirical confirmation (Uotila et al. 2009) | Strong |
All 54 experiments were implemented as Python agent-based simulations, each with a command-line interface supporting parameter sweeps and JSON output. Simulations are deterministic and reproducible: every experiment uses seed 42, and all results can be regenerated from the CLI commands documented in each findings file.
Each model implements a specific mechanism from the complexity economics literature: the SFI stock market uses genetic algorithm evolution of forecasting rules; the Beer Game uses Sterman's empirically calibrated anchor-and-adjust heuristic; Boolean networks use Kauffman's random Boolean functions with the Derrida parameter; the punctuated equilibrium model combines Jain-Krishna autocatalytic networks with Bak-Sneppen extremal dynamics; the rigids-vs-flexibles model uses Harrington's tournament promotion; the Prisoner's Dilemma uses Axelrod's spatial evolutionary framework; Sugarscape follows Epstein & Axtell; technology evolution uses NK fitness landscapes; and the integrated economy links four sub-models through composite health and dependency cascades.
Parameter sweeps were designed to isolate the effect of a single variable at a time (mutation rate, population size, connectivity, delay length, environmental stability), with the baseline configuration representing default or empirically calibrated values. Experiments range from 50 to 500 ticks depending on the model's convergence characteristics, with 200 ticks as the most common duration.
Empirical validation compares 23 simulation outputs against published measurements from peer-reviewed literature in finance, supply chain management, evolutionary biology, organizational science, and network theory. Match quality is assessed as Strong (within empirical range), Moderate (correct direction, approximate magnitude), or Weak (qualitative match only).