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Multi-Path Forecasting (MPF)

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Multi-Path Forecasting (MPF) is a formal model, based on Social contagion simulations and the Structural-demographic theory.[1], to predict the level of radicalization spread in societies. This approach was developed by Peter Turchin[2] to analyze how societies collapse and recover from polycrises[3] .

How it Works

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MPF can be implemented as an Agent-based model similar to the SIR model of viral spread[4], where each person or group in a society is represented as an "agent" that can be infected (becomes radicalized) or recover within social dynamics described by formal rules.

A MPF system consists of three main components:

  1. Contagion of Radical Ideas: This part looks at how radical ideas can spread through a population, similar to how a disease spreads. It divides people into three groups:
    1. Naive: People who are not yet radicalized but can be.
    2. Radicalized: People who hold extreme or revolutionary beliefs.
    3. Moderate: People who were once radicalized but have become less extreme (like being recovered from a disease).

Naive people can become radicalized if they are connected to radicalized people, and this is more likely to happen when there is more stress in society. Moderate people are like those who have "recovered" from radicalization.

  1. Political Stress Index: This part tries to measure how much stress or tension exists in a society. It looks at three main things that can increase this stress:
    1. Growing Inequality: When the gap between the rich and the poor gets bigger (measured as "growing inverse relative income").
    2. Young Population: When there is a large number of young adults (around age 23) in the population. This is sometimes called a "youth bulge."
    3. Too Many Elites: When the number of people competing for elite positions (like powerful jobs or high social status) grows faster than the population itself.

The model uses a formula to combine these factors into a single "political stress index." A higher index means a greater chance of radicalization spreading.

  1. Elite Dynamics: This part focuses on the behavior of the elite (the powerful or wealthy people in society). It considers:
    1. Income Differences: If the income of the elite is much higher than that of regular people, it can create instability.
    2. Social Mobility: The rate at which people can move up or down in social status. If there isn't much upward mobility, and there are many people who want to become elite but can't, these "surplus elites" are more likely to become radicalized (unless they become moderate instead).
    3. Equilibrium: The model assumes that in a stable society, the elite control a certain share of the wealth (for example, 10% of the total income for 1% of the population). When society moves away from this balance, it can lead to changes in the number of elites and their income.

The Role of Relative Income

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The most important factor driving change in the MPF model is relative income. This is how much income people have compared to others. The model tracks this over time.

  • If relative income for most people is high (close to the income of the elite), there is less pressure for change.
  • If relative income for most people goes down, and the gap between the rich and poor widens, it increases social stress and the likelihood of radicalization.

Key Factors in Radicalization

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The model suggests that the likelihood of radicalization spreading is influenced by:

  • Immiseration: A term for increasing poverty or worsening economic conditions for a large part of the population. This is linked to a decrease in relative income.
  • Age Structure: A large youth population can be more prone to instability and radicalization.
  • Elite Overproduction/Overcompetition: When too many people are competing for a limited number of elite positions, it can lead to frustration and radicalization among those who are left out.

Mathematical Formula for Radicalization Risk

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Turchin proposes a formula to estimate the risk of radicalization (α(t)):

α(t) = α0 + αw(w0 – w) + αe(e – e0) + α20

Where:

  • α0 is a base level of risk (0.1 in the model).
  • αw is the weight given to immiseration (1 in the model).
  • w0 is the initial relative income.
  • w is the current relative income.
  • αe is the weight given to elite overproduction (0.5 in the model).
  • e is the current share of the elite in the population.
  • e0 is the equilibrium share of the elite in the population (e.g., 0.01).
  • α20 represents the impact of a youth bulge.

This formula shows how changes in relative income and the size of the elite can affect the overall risk of radicalization in the society.

Usefulness of MPF

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MPF aims to provide a more comprehensive way to think about social change and potential instability. By modeling the interactions between different factors, it can help us understand:

  • How economic inequality can lead to social unrest.
  • The role of demographic changes (like youth bulges) in societal dynamics.
  • How competition within elites can contribute to instability.
  • The potential for radical ideas to spread under certain conditions.

By considering multiple factors and their interactions, MPF tries to offer a richer and more nuanced picture of possible future paths for a society, rather than a single, simple prediction[5]

References

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  1. ^ Goldstone, Jack A. (2017). "Demographic Structural Theory: 25 Years On". Cliodynamics. 8 (2). doi:10.21237/C7clio8237450.
  2. ^ Turchin, Peter; Witoszek, Nina; Thurner, Stefan; Garcia, David; Griffin, Roger; Hoyer, Daniel; Midttun, Atle; Bennett, James; Myrum Næss, Knut; Gavrilets, Sergey (2018). "A History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience". Cliodynamics. 9 (2). doi:10.21237/C7clio9242078.
  3. ^ Centeno, Miguel; Callahan, Peter; Larcey, Paul; Patterson, Thayer (2023-02-13). How Worlds Collapse: What History, Systems, and Complexity Can Teach Us About Our Modern World and Fragile Future (1 ed.). New York: Routledge. doi:10.4324/9781003331384. ISBN 978-1-003-33138-4.
  4. ^ Cooper, Ian; Mondal, Argha; Antonopoulos, Chris G. (2020-10-01). "A SIR model assumption for the spread of COVID-19 in different communities". Chaos, Solitons & Fractals. 139: 110057. arXiv:2006.10651. Bibcode:2020CSF...13910057C. doi:10.1016/j.chaos.2020.110057. ISSN 0960-0779. PMC 7321055. PMID 32834610.
  5. ^ Turchin, Peter; Korotayev, Andrey (2020-08-17). "The 2010 structural-demographic forecast for the 2010–2020 decade: A retrospective assessment". PLOS ONE. 15 (8): e0237458. Bibcode:2020PLoSO..1537458T. doi:10.1371/journal.pone.0237458. ISSN 1932-6203. PMC 7430736. PMID 32804982.