Communities can address wealth and income inequalities with different algorithms to distribute investment Capital. Today, the dominant algorithm to distribute Capital is the money increase approach, where a provider receives Capital, invests it, and receives more Capital back as profits.
Another approach is an algorithm where money is provided to a community to maximise investment from a given amount of money. The Community approach uses the profits to invest in more assets.
The provider approach maximises money for the investor, while the alternative maximises assets for the community.
Community Capital Markets are a way of maximising assets. For example, changing the Australian Energy Market to a Community Capital Market will reduce energy prices and increase investment in the community for a given amount of money. Over the past twenty years, the Australian Energy Market has increased investors' profits and prices to consumers. If instead, the Australian Energy Market was funded through a Community Capital Market, prices would have dropped, and we would have more renewables. The wealth disparity between the wealthy and the rest of the Australian population would also be less.
Wealth and Income Distribution.
The distribution of wealth in Australia is biased towards the wealthy. The top 10 percentile holds 61 times as much wealth as the bottom 10%. We expect the distribution to follow a normal curve with few outliers, but the distribution has more poor and very wealthy people than expected. The way Capital Markets operate causes the distribution bias towards the wealthy.
Note that changing to an unbiased Capital Market moves a relatively small amount of wealth of wealthy people to poorer people but makes a huge percentage difference in the wealth of the poor. An unbiased Capital Market is more efficient; the efficiencies will mean the rich keep their wealth while the poor get relatively richer. An efficient market means more investment. If the investment reduces costs, it typically reduces the demand for natural resources. Finding an algorithm or set of rules for Capital distribution to address these problems will address many economic and social issues.
Australian Income distribution is biased towards a flatter-than-expected distribution. The bias comes from the distribution of tax to lower-income people. However, increasing Capital for the poor means fewer tax transfers. Unbiased systems are typically more efficient, so wealth to poorer people will give them time and resources to do other things to reduce their cost of living. The algorithm to move money through taxes is expensive and inefficient. Society will have more funds to spend by reducing the need for some income distribution.
Free, unbiased markets are efficient markets meaning the distribution (sharing) of goods and services wastes less. The less waste, the less impact on the environment.
A Living Economy
The economy behaves like an ecosystem of living entities. Pagel’s — Wired for Culture — describes the evolution of social behaviour, including economics. His Gifford Lecture series describes social evolution.
Leading evolutionary theorist David Sloan Wilson and influential economist Dennis Snower have long advocated for an improved understanding of economics as a complex system. They outline a similar approach to Pagel in this RSA discussion.
Dennis Snower is an economist and the co-founder of Prosocial World, who has an interesting This View of Life Magazine.
David Sloan Wilson is the other co-founder of Prosocial and is a leading evolutionary biologist and is part of the Global Solutions Initiative.
Most financial algorithms are based on ideas from the study of mechanical systems where interactions are known and predictable. Living systems act like complex adaptive systems where interactions change the behaviour of the components. Changing social algorithms using ideas from Complex Adaptive Systems will result in stronger more reliable and lower-cost social systems.
Modelling the Economy as a Complex Adaptive System.
A Complex Adaptive System (CAS) is composed of multiple interconnected agents that can interact with each other and adapt to their environment. These agents can be humans, animals, organisations, or even technological systems, and they operate based on simple rules. A CAS's behaviour arises from its agents' interactions and adaptations, creating a global pattern that is not specified in advance. This pattern of behaviour is often referred to as emergent behaviour, a defining characteristic of CAS. — ChatGPT Definition
Modelling Tools for CAS (from ChatGPT)
- Agent-Based Modeling (ABM): This is a bottom-up approach to modelling complex systems that focuses on individual agents and their interactions. In ABM, each agent is modelled as an autonomous entity with its own rules, and the system’s behaviour as a whole arises from the interactions between these agents.
- System Dynamics Modeling: This is a top-down approach to modelling complex systems that uses feedback loops to represent the interactions between different system elements. System dynamics models often study complex systems’ long-term behaviour and identify potential levers for change.
- Network Analysis: This is a tool for understanding the structure and behaviour of complex networks, such as social networks, transportation networks, and biological networks. Network analysis can identify key nodes and pathways in a network and study how information spreads, and decisions are made in the network.
- Cellular Automata: This mathematical model is used to study complex systems that evolve. In cellular automata, a grid of cells represents a system. The state of each cell is updated based on a set of rules that depend on the state of the cells in its neighbourhood.
- Genetic Algorithms: This computational algorithm is inspired by the principles of evolution and natural selection. Genetic algorithms are used to solve optimization problems by evolving a population of candidate solutions over time.
- Monte Carlo simulations: This is a statistical method used to model the behaviour of complex systems. Monte Carlo simulations involve generating random samples from a probability distribution to simulate the system’s behaviour over many iterations.
- Game theory: This is a mathematical framework used to model decision-making in situations where multiple agents interact with each other. Game theory can be used to study the behaviour of complex adaptive systems, such as markets and social networks, where decisions made by one agent can affect the behaviour of others.
- Partial differential equations (PDEs): These mathematical equation models systems that change over time and space. PDEs can be used to model complex systems in fields such as physics, engineering, and biology.
- Markov Chain Monte Carlo (MCMC): This is a statistical method used to simulate the behaviour of complex systems by generating a sequence of random samples from a probability distribution. MCMC is often used to estimate the parameters of complex models, such as those used in Bayesian statistics.
- Reinforcement learning: This machine learning algorithm is used to train agents to make decisions in complex environments. Reinforcement learning can be used to model the behaviour of complex adaptive systems, such as robotics and autonomous systems.
- Hill Climbing: This is a simple search algorithm that iteratively moves to a neighbouring solution better than the current solution until a local optimum is found. Hill climbing can be used to model the behaviour of agents that make decisions based on locally optimised solutions.
- Simulated Annealing: A search algorithm inspired by the annealing process used in metallurgy. Simulated annealing allows for some “bad” moves to escape from local optima and find a globally optimal solution.
- Particle Swarm Optimization: This is a search algorithm that models the behaviour of a swarm of particles that move through a search space in search of an optimal solution. Particle swarm optimization can be used to model the behaviour of agents that communicate and coordinate with each other in a complex adaptive system.
An Example of a CAS Algorithm
Agent-Based Algorithms can both model and build an economy.
A Community Capital Market is an Agent-Based Algorithm to minimise the transfer of Capital within a Community for a given investment value. The current economy uses an algorithm with a Capital Market separate from the market in goods and services, resulting in less investment for a given amount of money. Capital Market profits give traders a share of future profits where the profits come from higher prices to consumers resulting in inflation. With a Community Capital Market, the extra investment goes to consumers, reducing the cost of products through less need for the products and lower prices.
Over the past twenty years, the Australian Energy Market illustrates the difference between the current approach to designing markets and a complex adaptive system approach. A reason for higher energy prices is the effect of the regulated distribution and transmission businesses taking Capital out of the industry and replacing it with higher prices, not in improved efficiency from investment.
Changing the Australian Energy Market to a Community Capital Market will reduce energy prices from the increased investment in Rewiring Australia. Consumers will invest in infrastructure to reduce consumption and increase the use of cheaper renewables within their own homes. The effect will be immediate and ongoing. The change will reduce the Rate of Return on new investments while increasing the rate of investing. It increases the number of people operating in the Capital Market by consumers receiving a share in future profits with each payment.
To illustrate the effect of a complex adaptive system on investment in the electricity system, the following describes how the government can distribute funds to invest in Community Batteries.
Modelling Community Battery Funding.
This model compares government funding for a $100,000 Community Battery through a Service Provider and where the user community serviced by the Battery receives the $100,000. The $100,000 covers the cost of installation, maintenance over the life of the Battery, depreciation and taxes. Each year the Battery generates $5,000 in income. In the first case, the income generated goes to the service provider to cover the operating costs of the investment. In the second case, the $5,000 is available to the community to reinvest as operating costs are covered with new Capital from the Community.
In the first case, the community receives no direct financial return. In the second case, the Community receives the $5,000 and invests it in other Community Assets using a Community Capital Market. Today Non-Battery Community Assets have a payback period of five years as they earn 20% per year in savings at current electricity prices. The earnings are all invested in the Community. After ten years, a Community owns the Batteries plus another $103,995 in assets. Importantly the bias in Capital Markets is redressed, and wealth is spread equitably across the Community.
If the Community or the government invested $50,000 in other more profitable Community Assets, the Community would end up with $361,984 in assets after ten years for an investment of $150,000.
The example illustrates the power of investing returns on investments rather than consuming the returns.
In the first Service Provider scenario, the investors receive $5,000 as dividends to spend on consumption. The total returns would be $50,000 compared to $103,995 from the second scenario, where Community Capital Markets ensure the investment of profits. Community members who wish to cash their returns must sell to other community members leaving the assets intact.
Ensuring consumers receive some of the profits removes most of the bias in the Capital Market, leading to a flatter distribution of wealth.
The change in the algorithm makes the difference — not any change in the electricity system or investments. It shows the efficacy of operating and designing economic systems as Complex Adaptable Systems.
The algorithm used was based on Agent-Based Modelling. It was developed through trial and error and came from modelling and real-world experiments. There are many more algorithms to apply to different economic scenarios to improve the operation of financial and other social systems. The approach may improve the efficiency of Capital Markets to provide enough Capital to adapt to the changing environment caused by human economic activity.
