Loyalty Model Simulation Lab: Agent-Based Behavioral Comparison

Research Simulation Lab

Loyalty Model Simulation Lab

Agent-based Monte Carlo comparison of baseline models vs network-enhanced approaches. All parameters are adjustable - test your own scenarios.

Methodology

Each simulation initializes agents with behavioral archetypes, then runs weekly time steps where agents make probabilistic decisions based on published research (Kahneman & Tversky, Nunes & Dreze, Kivetz et al., Metcalfe's Law). Both models run under identical initial conditions for fair comparison.

Mode
Preset
Horizon
Holders

Token Holder Archetypes

Inactive Holder
35%
Mercenary
30%
Speculator
20%
Diamond Hands
15%

Distribution affects cliff severity and long-term retention

Model A: Traditional Staking

Lock-up Duration 4w
Market Volatility Med
Network Partners 0
Streaks

Model B: Loyalteez Token Model

Utility Adoption 50%
Network Growth Mod
Market Volatility Med
Network Partners
12
Streaks
Re-engage Multiplier
2.0x

Simulation Results

Configure parameters and run simulation

Agent-based Monte Carlo comparison - all parameters are adjustable

Behavioral Science Foundation

Kahneman & Tversky

Loss Aversion (1979)

Losses perceived 2.25x more strongly than gains

Nunes & Dreze

Endowed Progress (2006)

34% higher completion with head start

Kivetz et al.

Goal Gradient (2006)

Effort accelerates near reward thresholds

Lally et al.

Habit Formation (2010)

Average 66 days to form a new habit

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