Decoding Pareto: How Agent-Based Technology Can Unveil New Perspectives and Solutions on Issues such as Inequality

In a world grappling with escalating multiple complex challenges, understanding their dynamics and seeking effective remedies in a more granular way is more crucial than ever if we ever want to see systemic change and improvement to peoples’ lives. Take an issue such inequality for example, which politicians, theorists, and activists have debated, at times violently, over. The journey to measure inequality has been complex. Traditional methods often focused on either absolute or relative inequality. Absolute inequality deals with the disparity in actual resources distributed among individuals, while relative inequality examines the distribution in relation to society’s overall economic structure.

This debate between smoothing out relative versus absolute inequality has been central to policy discussions. Some advocate for improving the poorest’s absolute conditions, while others argue that reducing relative disparities is key to maintaining social harmony and economic stability. Often the debate reaches a kind of stalemate, or we see the pendulum swinging between one side and another, without every reaching a long-term lasting solutions. Well one promising approach may now be upon us, to gain better insight, and hopefully make more of a real impact on the issue of inequality, and by extension poverty as well as the economic wellbeing of entire societies.

Prof. Jean Phillipe Bouchard’s groundbreaking work in simulating glass molecule phase transitions over a decade ago has led to the development of agent-based models on inequality – something that previously was not possible. Prior to this, nobody really understood how inequality arises from a mathematical or computational point of view. These models revolutionize our understanding of inequality. They typically start with agents possessing equal wealth. As these agents engage in economic activities, experiencing random returns on investments and varying degrees of reinvestment, a pattern of inequality rapidly emerges. In just a few generations, these models can show extreme wealth concentration, with a minority, or even a single individual, amassing the majority of wealth.

This modelling has illuminated that neither traditionally left nor right-wing economic strategies fully address inequality’s roots. Accelerating the economy can exacerbate inequality, while redistributive efforts by states or other entities don’t address underlying causes. This phenomenon is observed in diverse settings, from totalitarian regimes to extreme environments like concentration camps. Incorporating behavioural economics into these models brings to light how entrenched wealth or poverty across generations can influence decision-making, further deepening inequality and a lack of social mobility. These models can also be enhanced to account for the recent impacts of technology and geographical factors.

A key insight from recent research is the impact of network interconnectedness on social mobility. People with wider, more diverse networks tend to have greater social mobility, leveraging their connections for knowledge and opportunity exchange. This underscores the potential of initiatives like Teach First, angel investing, and even Royal Patronage, especially when combined with training and support. Such initiatives can bridge the gap between the wealthy and those with lower incomes, fostering trade and the sharing of life-changing ideas and opportunities. It turns out that potentially we can help smooth inequality not just through free markets and state-led redistribution, but by helping everyone build better networks and connection.

A new consortium is being developed called 4Impact which aims to harness tools such as Agent-Based Modelling, smarter impact measurement, and predictive tools to help policy- and change-makers between design initiatives and policies that would make more of a difference to issues such as inequality, as well as a host of other phenomena and crises we face. It will seek to support multiple actors who want to help shape global and local systems and tackle challenges in ways that have greater nuance and effectiveness than current approaches, especially in areas of greatest need and frontier markets. By harnessing tools such as ABM we might not still always have the perfect, exact answer, which will often always be a pipedream – but we can better understand scenarios, prepare better for crises, and prioritise efforts to address issues like poverty and inequality in interdisciplinary ways that are practical, and more likely to connect with the real life experiences of those on the front line, and in the communities affected.