1 Ulf Christian Ewert, Mathias Roehl and Adelinde Uhrmacher Max Planck Institute for Demographic Research Doberaner Str. 114 D-18057 Rostock Germany Demographic and economic consequences of mortality crises in pre-modern Europe Does agent-based simulation help to gain new insights into the course of recovery-from-desaster processes? University of Rostock, Department of Computer Science, Research Group Modelling and Simulation Albert-Einstein-Straße 21 D-18059 Rostock Germany

2 Historical representation of the topic „Die erschreckliche Wasser-Fluth.” [The horrible storm tide], taken from Happel, „Die größten Denkwürdigkeiten der Welt”, 1683. Definition Mortality crisis is a decline of population that is  mortality induced,  excessive,  rapid,  has presumably negative demographic and economic consequences Modern scientific representation

3 Causes of mortality crises  What are causes of mortality crises?  epidemics, famines, natural desasters, wars  mortality crisis is a structural element of the history of pre-modern Europe!  Selected example: The Low Countries during late Middle Ages  bubonic plagues (1348, 1400)  famines (1315/17; 1407/08; 1437/40)  heavy storm tides (1362; 1436)  casualities of war (“the Hundred Year’s War”)  local desasters (fire in Lille, 1400)

4 Consequences of mortality crises  What sort of consequences do we expect?  demographic consequences: sharp fertility decline (increase in fertility) distortion of age and sex ratios  economic consequences: paralysis of local markets sharp increase in food prices increase of nominal wages (per capita income)  cultural consequences: superstitions anticipation of apocalypse

5 Causal model  Causal interactions crisis management precautionary measures crisis management having grain in stock closing the town; construction of dikes; defending the town attracting in-migration abolishing marriage regulations intervening in market processes starting job-creation programmes

6 Focus of the analysis  What is the focus when studying mortality crises?  analysis of the course of the recovery process  assessment of effects of distorted age and sex ratios  judgement of the role of crisis management in overcoming negative consequences of the crisis  Why can the study of mortality crises be useful for today’s Demography?  comparison to consequences of current desasters in developing countries

7 Appropriateness of agent-based modelling  Why agent-based modelling is appropriate to study such crises?  trade-off between historical accuracy and structural simplicity  agent-based modelling enables to distinguish several sorts of reaction patterns to the crisis!  Example: Medieval and Early Modern Towns  craftsmen: nobody wants to buy their products, marriage plans will be delayed  labourers: real wages are raising, marriage plans can be executed

8 Actors, Systems, Interactions  Actors  merchants  craftsmen  labourers  local authority  Systems  grain market  consumer good market  labour market  marriage market  public opinion  environment of town  represents demographic and economic developments outside of the town  represents the supply of goods to the town  import and sell grain  seek to maximize their profit  have to find marriage partners  produce and sell goods  seek to maximize their profit  have to find marriage partners  supply labour to craftsmen  seek to maxime their savings  have to find marriage partners  seeks to keep order  intervenes in market processes  changes market regulations  changes marriage norms  implements measures to attract in-migration  represents the supply of food to the town  represents the working relations in the town  stratified segmentation of actors  strongly regulated by norms  emerges from actors’ satisfaction  represents degree of order

9  Modelling Actors, Systems & Interactions Modelling approach  framework for modelling & simulation  separation between institutions and individuals  acting by communication

10  Modelling institutions Model  macro level view  economic, mathematical models

11  Modelling the population – composition  actor groups  decision processes Model

12  Modelling the population – classification  utility-based decision making (quantitative)  planning (qualitative, symbolic) Model

13  A deliberative agent : The Local Authority  resource bounded  BDI-architecture Agent architecture

14  A decision situation  current situation: all actor groups are unsatisfied supply of grain and labour is too low in the town supply of consumer goods is sufficient Local Authority has little money and much grain available  selected goals: all actor groups should be satisfied sufficient supply of labour, grain and goods Local Authority has still some money available Sample scenario

15  Developed intentions Deliberation result

16  Where do we stand? State-of-the-art  agents (local authority): specification of beliefs, desires, plan operators integration of general planning system (GraphPlan) planning experiments: exploring the interplay between beliefs, desires, plan operators  actors (merchants, craftsmen, labourers): modelling of utility-based decision rules but not yet tested  institutions (markets, public opinion, environment of the town): modelling of general structure but not yet tested

17  Implementation in JAMES  sound system theoretic foundation (DEVS)  a J ava-based A gent M odelling E nvironment for S imulation  clear separation between model & simulation  modular hierarchical composition  parallel, distributed execution  variable structure models Simulation

18  Does agent-based simulation help to gain new insights into the course of recovery-from-desaster processes? Prospects of the model  What are future prospects of the model?  reproduction of recovery-from-desaster processes on the basis of micro-macro-level interactions  measurement of the relative impact of demographic and economic distortions  comparison of recovery processes due to different causes and characteristics of the crisis  comparison of scenarios with various degrees of intervention by the local authority  simulation of sequential desasters with learning agents (local authority)