Essential design constraint: At all points, the system must vector towards omni-win-win dynamics. (At no points can it incent win-lose dynamics.)
Understanding win-lose dynamics:
Win-lose taxonomy: Obsoleting the impulse towards win-lose dynamics systemically requires obsoleting each category of win-lose dynamic:
- Agent vs other agents
- Agent vs the system/commons
- Agent vs themselves (parts conflicts)
Win-lose generator function: all types of win-lose dynamics result from the perception of separable interest. Ie, from perceiving various values/agents/metrics within the system as being irreconcilably dichotomous.
Win-lose system processes: in a fundamentally win-lose system where various agents/goals/values are seen as dichotomous and thus competing for scarce resource, the best macro dynamics are achieved through processes of optimization (in the allocation of scarce resource) and game theoretic equilibria (of choice patterns for competitive agents).
Win-lose upper bounds: The following are hard upper limits to system abundance in win-lose systems:
- In non-cooperative games, the ideal system-equilibria points (eg Nash Equilibria) are generally worse for all players than the global optimum, which is unstable.
- The global optimum is generally significantly worse than what could be achieved by changing the context to a cooperative game.
- Finding equilibrium points in complex systems is generally impossible (NP complete).
- The difficulty of complexity (when all the agents/values/metrics are competing for scarce resource) creates a need to simplify, leading to reductionistic models and value metrics, leading to externality.
- Where information creates advantage, win-lose dynamics will incent disinformation, leading to decreased system coherence and increased entropy.
Understanding win-win dynamics:
Win-win taxonomy: Creating the impulse towards win-win dynamics systemically requires creating win-win dynamics in each category:
- Alignment of the well-being of agents with each other
- Alignment of the well-being of agents with the commons
- Alignment of the well-being of agents with themselves (resolving parts conflicts)
Win-win generator function: win-win dynamics arise from the perception of symbiotic/interconnected interest. Ie, from perceiving various values/agents/metrics within the system as dialectics to be simultaneously (synergistically) supported via a higher order synthesis process.
Win-Win system processes: in a fundamentally win-win system where various agents/goals/values are seen as interconnected and thus inter-benefitable, the best macro dynamics are achieved through an integrated design process, where each of the various agents/values/goals are taken as design constraints to be factored simultaneously, synergies sought and maximized, and a whole-system integrated design process engaged in that results in optimum system integrity.
Win-win lower bounds:
- Integrated system design processes have no irreconcilably mismatched incentives, so all movement is towards global optimum.
- The increased resource advantage of obsoleting unnecessary entropy and maximizing positive synergies means the global design result will always be more totally advantageous than the global gaming optimum.
- An integrated design process does not require NP computation.
- Design requires having all the constraints clear first, which is an impulse away from reductionism and towards information fidelity.
- In a win-win system engaging in design, disinformation is always disadvantageous to all involved, and transparency is optimally incentivized.
Side by side distinctions:
- Win-lose games require and create high entropy.
- Win-win games require and create high synergy.
- Win-lose games require and create turbulent flows.
- Win-win games require and create laminar flows.
- Win-lose games require and incent disinformation.
- Win-win games require and incent transparency and vetting.
- Win-lose games must have very narrow value metrics.
- Win-win games have increasingly and unboundedly complex value metrics.