Modeling Societal Dynamics with Historical Data

Summary:
Focus: how do societies function and how do groups of humans work and evolve?

• Many patterns and underlying dynamics in today’s society are similar to how they have worked in the past

  • Coordination

  • Resource management

  • Interaction with environment

• Observation: most past societies and civilizations don’t exist today

  • Driven into collapse by common challenges

  • Many lessons to be learned from this

• Seshat databank (https://seshat-db.com/)

  • 100s of years of data on societies 

  • Since 2011

• Societal Dynamics (SoDY): translates this into relevant insights

• Turning history into data

  • Cliodynamics: 

    • Scientific approach to study history

    • Theory testing, statistical analysis, model building

    • Big quantitative data + deeper quantitative descriptions

  • Approach:

    • Focus on region in the world

    • Track the sequence of polities that existed at that location

    • Analysis: develop codebook, consult with experts

    • Quantify “social complexity”

      • 51 variables drawn from various theories about social complexities

        • E.g. monetary system, legal code, population

        • Tracked over time within and across polities

      • 400+ societies from late neolithic to early modern

    • Analyze, test theories, develop causal models

      • Example: do changes in military technology cause polities to increase in area?

      • Track data for different societies

      • Compare to simulations that encode the dynamics predicted by theories

      • Observation: increases in social complexity driven primarily by transition to sedentarism, agricultural productivity, war

• Model: Nomads-Agrarians

  • Spread of societies in Eurasia 1500 BC - 1500 AD

  • Historical Observations:

    • ~750 BC the land controlled by polities started rising

    • More states over 3000 years concentrate in Egypt/Sumeria/China/Turkey region, with many less dense regions (Europe, Russian steppe, sub-Sahara)

    • Larger polities, even very large empires, don’t last longer

  • Agent-based model

    • Types: Agrarian states and Nomadic confederations

    • Generated synthetic history with geography, technology, wars, competition

    • Investigate impact and timing of a few exogenous contingencies

      • Regional agricultural productivity

      • Asymmetrical threats from military innovations

      • Regional efficient military transport improvements

  • Observations from model:

    • Agrarian states:

      • 5% of productivity is reserved for warriors; actual warrior population varies between 1-3% during growth, then starts to saturate the surplus, thus limiting the state’s growth

      • Dynamic: occupy more land -> produce more warriors -> more productive -> occupy more land

      • Once warriors can no longer expand the state (military logistics constraint) they saturate the new surplus and start to fight each other; then start a civil war, producing a split

    • Asymmetric military shock:

      • Nomads provides the shock; much smaller population compared to agrarian states

      • Develop new cavalry tactics (horses, compound bows) that defeat sedentary armies of agrarian states

      • Agrarian states adopt same technologies: auto-catalytic power race between the two (mirror empires along the steppe-agrarian border)

      • Simulation: 

        • ~50 Central Asian nomadic tribes in 1500 BC

        • Nomads never annex agrarian state land and vice versa

        • Nomads can confederate with each other, confederations collapse stochastically

        • Co-evolution where both nomads and agrarians get stronger and larger, with border groups taking over their deeper neighbors

        • Agrarian states learn from nomads and each other

        • Model predicts overall 3,000 years of history roughly correctly, capturing the development of steppe empires, China, Europe, etc. but with some error in predicting population; distribution of state sizes is mostly right

        • Some parameters can be estimated directly from history, others relatively: military logistical efficiency; need to be calibrated to ensure agreement with overall historical record

          • Estimated from data: agricultural productivity, strike depth of nomads

          • Estimated relatively: military logistical efficiency

• Model: Demographic cycling in Europe during mid-Holocene

  • Populations move from Turkey into Europe -> France -> Atlantic islands over 4-5k years in multiple pulses

  •  Observed dynamic of initial population growth -> decline -> regrowth

  • After a population moves in there is a drop in population ~400 years later, followed by slow regrowth over following millenia

  • Hypotheses for what drives the boom and bust pattern:

    • Booms and busts driven by changes in climate

      • Don’t find long-term shifts that explain these collapses

    • Driven by inter-village conflict

      • Populations move out to new lands

      • Remaining populations don’t have anywhere to expand to, turn into aggressors who attack the villages that moved out earlier

      • After war period, return to stability in a more empty land

      • Model of this dynamic is more consistent with archeological evidence

• Future

  • Dynamics of crisis

  • Seshat Databank’s CrisisDataBase Project

    • What drives crises?

    • What can be done about it?

    • Quantify social pressures, societal responses (e.g. revolution), and post-crisis trajectories (recovery, continual instability, collapse)

    • What factors determine the ultimate outcomes of crises?

  • Can we rewrite the history of the near future

    • Multi-path forecasting

    • Use current state and historical developments from ~10-50 years prior to make near-term forecasts

    • Using dynamic / complex systems framework; 'cycles' are emergent under certain conditions, but not essential (in some conditions spikes of discord don't cycle - one of the ways the perspective differs from Howe & Strauss work cycles of discord modeling framework

    • Analyze possible policy interventions through scenario exploration simulations