The Integrated History and future Of People on Earth – IHOPE
IHOPE studies how societies of different size and structure have co-evolved with their environments over the long time, from their first emergence to their collapse, causing and dealing with environmental change. The data and information from the past are then used to build dynamical models of the processes that have shaped that co-evolution, and those models are used to learn for the future by experimentally modifying them, stressing them and otherwise studying how the dynamics would play out under ranges of different circumstances. Comparing these scenario’s teaches us about the structure, strengths and weaknesses of the dynamical systems involved, their potential tipping points, and also allows us to study the unintended consequences of different human responses to any set of circumstances.
Currently, IHOPE is using the following as case studies:
- Australian hunter-gatherer societies,
- small-scale dry-farming and irrigations societies in the SW of the ,
- the more complex chiefly societies of the Maya area,
- the Roman Empire.
Future case studies
- China and SE Asia,
- the islands in the Caribbean and the Pacific,
- the circum-Arctic zone.
IHOPE studies the long-term trajectories of integrated human-environmental systems based on archaeological, palaeo-environmental, anthropological and historical data. The choice to extend the temporal scale is enabled by reducing the spatial scale, and offers the opportunity to observe the following:
- The very slow, very long-term dynamics, that are not observable at shorter timescales.
- A much wider range of system states than is observable over the short term.
- The second order dynamics of the socio-environmental co-evolution: how the dynamics governing that co-evolution have themselves changed as the societies involved and their environments became more and more intricately intertwined.
Maya Case study
The MayaSim model is constructed using the software Netlogo (Wilenski et al. 1999). It examines the relationship between population growth, agricultural production, pressure on ecosystem services, forest succession, value of trade, and the stability of trade networks. These combine to allow agents representing Maya settlements to develop and expand within a landscape that changes under climate variation and responds to anthropogenic pressure. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya.
The simulation begins with calculations of biophysical variables for precipitation, water flow, and net primary productivity, and these are then used to calculate forest succession, agricultural production, and ecosystem services. Settlement agents interact with the spatial landscape to generate agricultural yield through cropping, derive benefit from local ecosystem services, and generate trade benefits within their local trade network. The combined benefits of agriculture, ecosystem services and trade drive demographic growth including migration.
By setting the parameters (temperature, hydrology, area available for cultivation, crops, population) differently, different scenarios can be elaborated for the evolution of Maya societies. These can, for example, relate to energy demand and consumption, as is done below:
- High Resources/ Low Demand – rapid adaptations to exploit excess energy
- High Resources/ Moderate Demand – Stable period, able to adapt or withstand changes in resources or demand
- Moderate Resources/ Low Demand- makes changes in social organization, population growth, territorial control
- Moderate Resources/ Moderate Demand – unstable period, “quick-fix” changes in demand or resources This band only exists briefly.
- Moderate Resources/ High Demand – rapid adaptations to declining supply; consumption of future resources to meet needs
- Low Resources/ High Demand- highly susceptible to any climate, social or environmental
A regression analysis over three principal factors then gives the relationships for different groups of settlements (illustrated in the figure to the left). Each dot is a city (and the surrounding landscape on which it depends), and the yellow circles indicate four different bands of energy availability and consumption.
We expect that thus exploring the possible alternative attractors around the historical ‘real-life’ case studies will multiply the number of scenarios that we can investigate in order to better understand how we have come, as a society, to be in the predicament we are currently in.