1. Two Modeling Cultures Marco Janssen School of Sustainability
Center for Behavior, Institutions and the Environment
Arizona State University
In this presentation I will talk about the challenges to include social science into computational models of human-environmental interactions. I do this from the perspective of my experiences during the last 25 years in working in and with different disciplines. The talk is entitles Two modeling cultures, to reflect the different types of practices between the social and natural sciences.
In this talk I will make some general statements about research in different fields. I very aware that will exaggerate quite a bit and that the differences are not so black and white.

2. Outline
Background
Brief history of integrated global models of humans and the environment.
The challenges of getting social science represented in integrated models.
Two examples: Hunter-gather & Water management in Mexico City
Conclusions

3. "the intellectual life of the whole of western society" was split into the titular two cultures — namely the sciences and the humanities (1959) 
Two modeling cultures: “Social” and “natural” sciences have different ‘cultures’ in the use of mathematics and modeling. When earth system scientists connect to social science there might be a lack understanding which may hinder a productive collaboration.
Refer to a famous essay by C.P. Snow entitled Two cultures. He referred to the quite distinct cultures among the humanities and the sciences without much interaction. This essay got a lot of attention, and a lot of critique. I do not want to state that social and natural sciences live aside from each other and do not interact. But my experience is that there are quite different cultures that are not always well understood about each other. For example, many social scientists do not accept the use of quantitative methods to describe social phenomena. They would argue that understanding of social phenomena is based on individual experiences and the way to describe them are narratives. There are good reasons to do so, but it hinders to include their knowledge into integrated models, which is something we care about. Social scientists are typically not well trained in computational approaches beyond some basic statistics, and the lack of capacity hinders social scientists to get involved with modeling even if they see the relevance of it.
If earth system scientists likes to connect more to the social sciences it would be helpful to understand some of those cultural biases.

4. My background Formal training: Operations Research
: Researcher on Integrated Assessment Modeling of Climate Change (IMAGE group).
: Postdoc: Environmental Economics
: Research Scientist: Political and Behavioral Economics
: Ass/Assoc Department of Anthropology (School of Human Evolution and Social Change), Arizona State University
2015-: School of Sustainability
Since this presentation is informed by my experiences, I will tell you a bit about my background. I got my formal training in operations research, and was a researcher in at the Dutch EPA as part of the IMAGE team, where IMAGE is one of the well known Integrated Assessment Models. I did my PhD in mathematics during that period. As my interests shifted to including more social science into models of human environmental interactions, I did a postdocs in environmental, political and behavioral economics. I started working with scholars from diverse social sciences especially economics, political science and psychology. I ended up with a tenure track position in a Department of Anthropology at ASU, while I never took any formal anthropology course. The Department was in a transition to a more interdisciplinary social science school, and my hire was part of this process. Currently I am a professor in the School of Sustainability at ASU.
I will now shift gears and discuss some of the history of integrated models of human environmental interactions.

5. Integrated Modeling
World 3 model: Limits to Growth report by Dennis Meadows et al.
A study that most of you will know is done with the World 3 model by Dennis Meadows and his colleagues about 45 years ago. This was a system dynamics model at the global level describing the dynamics of a few set of broad categories of stocks. As illustrated in this figure we may experience major resource scarcity and pollution in the coming decades. Although this was one of the scenarios, the doom scenarios, the data since 1972 follows closely this path. When Meadows et al did their studies, they also provided scenarios in which the system would not collapse. Those stabilizing scenarios cannot be generated with the pathway the global system experienced during the last 45 years.

6. Critique Subjective assumptions driving the results.
Nordhaus, W.D. (1973), World Dynamics: Measurement without Data, The Economic Journal,
Cole, H.S.D, C. Freeman, M. Jahoda and K.L.R. Pavitt (eds.) (1973), Models of Doom - a critique of The Limits to Growth’, Universe Books, New York, USA.
The Study on the Limits to growth got a lot of attention and a lot of critique. Much of the critique was focused on the lack of empirical grounding of the model and the subjective assumptions that were made and affected the model results. Both the social and natural science were highly simplistic. Meadows et al. were aware of those shortcomings and therefore provided substantial sensitivity analysis, but in the end the doom scenario was what sticked to the broader audience. I think we need to see this model as an eye-opener, not as model to study the details of the human-environmental relationships.

7. Integrated Assessment Models Rotmans, Alcamo et al
Integrated Assessment Models Rotmans, Alcamo et al. Human activities via scenarios
There was some follow up on the Meadows et al. study including regionalized versions, but the next major step in integrated modeling of human environmental systems was the development of integrated assessment models to study climate change. Those models started to appear around For example, the IMAGE model basically calculates the impact from socio-economic scenarios on the climate systems. The integration is the coupling of simplified versions of the material flows in the earth system. Given was economic growth and population change, which lead to greenhouse gas emissions. Then one uses simplified versions of more sophisticated natural science models to calculate the climate changes and its impacts.

8. DICE Dynamic Integrated Climate-Economy
Economist Nordhaus.
Optimization of discounted sum of long term welfare
Tight feedback between actions and impact.
The human component was basically absent in the IMAGE modeling, besides some simple land use modeling. A very different example is DICE from economist Bill Nordhaus which consist of a handful of equations which are used to determine the optimal investment strategy to maximize the discounted welfare. The representative agent has full understanding of the climate system and maximize the discounted welfare accumulated over the next 200 years. Maybe because of it’s simplicity the model has been influential in policy making.

9. Two types of IAMs
Drivers of population, technology and economics, lead to emissions and climate change.
By simplifying the climate system, calculate optimal response assuming we have control.
Can we capture both complexity of social and environmental systems?
So both approaches are called integrated assessment models, but both are not integrating the social sciences beyond some basic economic indicators, and both do not integrate feedbacks between the social and the environmental systems. This was the state of the IAMs when I was working in that field 20 years ago, and to my knowledge this is still the divide. It is great to see the increased interest to connect a broader spectrum of social science models with the earth system science models, but is this possible. What kind of social science models do earth system scientists expect to be coupled to, and what kind of models do social scientists actually work on?

10. Adaptive agents
Combining economics of DICE and climate system of IMAGE.
Investment and emission policy driven by learning given that the world functions according to a specific perspective.
In my dissertation I tried to combine components of the DICE and the IMAGE model, as well as concepts from complex adaptive systems. I developed an agent-based model, with agents who had particular understandings of the climate system and particular goals for humanity, and dependent on the climate change they experienced they would adapt their policies. The future scenarios in this figure shows high emission levels with low temperature change since the climate change was not confirmed, while a world with high climate sensitivity lead agents to experience climate change quickly and may adapt their policies to a low emission future. The resulting model integrates both social and environmental components, but we also made up a lot up about the mechanisms of the social system.
Janssen, M.A. and H.J.M. de Vries (1998) The battle of perspectives:
a multi-agent model with adaptive responses to climate change, Ecological Economics 26(1):
43-65.

11. On the human component
So now we have seen a brief overview of some of the integrated models, I like to focus on why we do not see much more social science in those models. Some of you may argue that the earth system science in those models is also lacking or inadequate. I will not focus on that. I like to discuss some of the challenges that I found illustrative to social science that makes it sometimes hard to include the breath and depth of knowledge in the social sciences.

12. Some may argue that social science is included since economists have played such a dominant role, especially in climate change policy. It is important to realize that there is a lot of critique from within economics on the state of the economic modeling. The economic crisis in 2008 demonstrated many problems of the mainstream approach in economics focused on equilibria and rational decision making. There are now more funding via private foundations to support the alternative approaches which existed for a long time, but are still not mainstream.
Recall the critique on the World 3 model. Now the critique is again that the model results depend a lot on the subjective assumptions. Nordhaus was the economist who criticized the subjectivity of the World 3 model while his work on climate change economics is now criticized to be dependent on a subjective assumptions on the valuation of climate impacts. The critique on models like DICE have been substantial since the early 1990s, but now the critique also comes from economists themselves in top journal in economics.

13. Beyond mainstream economics
Behavioral economics, evolutionary economics, new institutional economics, ..
Psychology (social, neuro, environmental, ..)
Anthropology (cultural, physical, ..)
Sociology (political ecology, collective choice, ..)
History, philosophy, …
The standard economic model of selfish rational individuals making decisions with full knowledge of the dynamics of the system is not the approach that all economists have adopted. There are various streams in economics, including those more aligned with psychology and complex adaptive systems. There are also various types of psychology, sociology and anthropology. If you say you are a social scientists many non-social scientists assume you are a sociologists, but that is only one of the various disciplines.

14. Social science often generate obvious generalities
The large diversity of social science disciplines with their own theories and approaches indicates a broader problem. Physicist turned into sociologist Duncan Watts wrote an excellent book on this topic. If you hear the results from social science studies you may think that the results are rather obvious. However, at the start of the study there might have been alternative explanations that were equally plausible. Because of the fundamental uncertainty and context specific dynamics of the system, it is difficult to make specific generalizable predictions.
An example Duncan Watts uses is the Mona Lisa painting, one of the most famous paintings in the world. Why is this painting so famous. Was it because of the talent of Leonardo da Vinci or was it pure luck? Read the book to find out which of the two obvious answers seems the correct one.
Why is the Mona Lisa the most famous painting in the world? Quality or fluke?

15. Challenges for modellers
Many theories. Social scientists like to disagree with each other.
Theories are often narratives.
No community effort to test and falsify theories to come to the true theory.
Importance of context, interpretation and meaning.
The practices common in social sciences lead to certain problems if we want to make quantitative models. First there are many different theories and with the level of uncertainty common in social systems, there is not a strong hierarchy among the theories.
One problem with many of the theories, from the perspective of a modeler, is that many theories are narratives and require therefore interpretation by the modeler to create equations. As a modeler who is not a specialist in those theories we include our subjectivities and biases how to translate the qualitative narratives into computational algorithms. Perhaps the social scientists who create those narratives can create those equations. That is sometimes possible and there are excellent modelers among the social sciences. But many people who focus on narratives have not much interest to be involved with quantitative models. As some of my colleagues in anthropology mentioned, whom am I to create equations about how people think. This might also illustrate a lack of basic education in quantitative methods and misunderstanding of the value of modeling. One of the long term challenges will be to increase the capacity of social scientists to work with quantitative modelers.
Within the social sciences there is also not culture of systematic testing of theories and accumulate the evidence to define the true theory. There are many different theories and subfields and there is often not an overarching challenge that lead social scientists to collaborate and accumulate knowledge. There is not the Apollo project in social science. Many social scientist published single authored book, as is expected to get tenure. As such there is not a culture of collaboration in large international projects, another skill set that may need more attention in order to get them working within projects on modeling human-environmental systems.

16. Modeling human systems
Take into account the diversity of theories (robustness analysis).
Modeling is a subjective enterprise (so be transparent).
Specific questions
Do modeling projects advance social science?
Based on the experiences I have in doing modeling with a wide variety of social scientists, my advice is to test alternative plausible theories if possible. This is a kind of robustness check for the diversity of theories. We further have to acknowledge that modeling is a subjective enterprise. In fact it is often a reflection on how we, the modelers, approaches the problem. Moreover, to make a modeling exercise relevant it is recommended to focus on a specific question. This means that we do not make general human decision making models, but within a specific context. Finally, it will be good to ask what those modeling efforts on human-environmental systems, would contribute to the social science. Will social science just provide some basic drivers of environmental change, or do those modeling efforts advance social science?
I will now discuss two examples of modeling studies I have been involved in and how we try to balance the desire for simple transparent models, and context specific knowledge.

17. Hunting of Ache men
Based on transect data (encounter rates), observations (time spend hunting, success rates, pursuit time) over a 30 year period.
30 species, 5 minute resolution on 60,000 hectares.
The first example is a study I did with anthropologist Kim Hill. Kim Hill studies the indigenous group the Ache in Paraguay for more than 30 years. He studies a variety of questions related to optimal foraging theory, health issues, social networks, etc. He has an enormous amount of data collected over the years that may enable us to develop foraging models of human hunters that are based on empirical observations. The modeling project was focused on describing the hunting process to explain observed data.
The basic idea of the model is that actions of hunters are modeled at a 5 minute resolution in a landscape of 60,000 cells of 100 by 100 meters. We populate the landscape with 30 different species, where each species has a probability to be encountered in specific vegetation types.
When I started the project Kim Hill could talk for hours about anecdotes of hunting trips and how the Ache were able to be successful or not. He wanted to include a lot of complexity in my perspective. I secretively developed a very simple random walk model that was doing pretty well, and based on that we could test additional complexities. The resulting decision diagram is like follows and in line with the basic principles of optimal foraging theory:
Janssen, M.A. and K. Hill (2014) Human Ecology 42(6):

18. Agents make decisions about movement direction influenced by the location of the camp in the evening and the movement of other agents. Agents who encounter a species, decide whether it is worth the hunting effort, and if this is the case they may spend a few time steps in the hunting mode. They might be successful or not in the hunting activities. For some species there is collaborative hunting which increased the probability of success.

19. Random movements with 5 hunters per camp 0.92
Model
Correlation with data
0 model
0.87
Random movements
0.88
Random movements with 5 hunters per camp
0.92
Flocking behavior
0.93
Flocking behavior with cooperative hunting
0.97
We were able to get a very good fit with the data. Starting with a random walk model we increased the complexity of the model based on the knowledge of the expert Kim Hill, and we derived a better fit each time. Note that we did not do any parameter fitting. All parameter values are based on measurements in the field or educated guesses.

20. Tradeoff between amount and frequency
For the anthropologists it was critical that we make use of the detailed field observations and not do some data fitting. With the model constructed we could evaluate some questions, like what is the optimal group size of the Ache. We varied the group size in the simulated landscape and based on the tradeoffs of returns and risk we found that 7 hunters, which is about 30 people, is the optimal group size. This was indeed the group size observed in the field. It is also beneficial to move to a new camp location every day.

21. Conclusions
We can describe observed behavior and derive better insights of the system.
Model is used as a starting point for modeling hunter gathering in coastal South Africa 100,000 years ago. Besides hunting, we include material procurement, wood collection, shell fish, and plant gathering.
One of the reasons we developed this model is to be a starting point for a more advanced hunter-gathering model in Coastal South Africa, about 100,000 years ago. The agents will not only hunt, they also fish, gather roots and fruits, gather wood and collect stones for making tools. In this project we also have a climate model that generate past climates which are inputs for the vegetation models to generate a landscape where they agents are foraging. Obviously we do not have direct observations to test our models, but we can compare alternative foraging strategies impacting the distribution of objects found in the camp sites.

22. Flooding and Water scarcity in Mexico City
Capital of Mexico
8 million people (20M when counting the suburbs)
6th largest City in the world (Megacity)
Highly densified
High inequality
Hunter-gathering societies are relative simple compared to our modern urban societies. The second example, I briefly like to discuss is water management in Mexico city. Mexico city is build on a lake, and it should not be a surprise that there are flooding problems. There is also water scarcity since the poor neighborhoods, often illegal settlements, up the hills do not have water pipes coming to them. A lot of the water get imported from areas far away from Mexico City as well as extracted from the groundwater reservoirs. Ground water use leads also to subsidence and damage to infrastructure. The water authorities have not sufficient finance to maintain the infrastructure of the system. Finally, we can expect that climate change and a growing city will enhance the water challenges.

23. Megadapt project
The water governance is Mexico City is broken and there are no simple solutions. We are developing a suite of models including hydrological-climate models and a political economy model to explore possible future of Mexico city and interact with relevant stakeholders at city and neighborhood level to stimulate a discourse. The stakeholder engagement part is mainly done by our colleagues at UNAM.
The focus of the modeling effort is not to predict the future. We also have no expectation that we can capture the complex informal economy that is driving a lot of the dynamics in Mexico City. What we are doing is to simulate the interactions between 2000 neighborhoods and a central water authority with a limited budget. What are the priorities of the water authority? How does this effect the investment decisions? And how will this affect the neighborhoods? Will neighborhoods protest and/or adapt? And how does this affect the long term decisions of the water authority.

24. This figures shows the basic cycle of actions in the model
This figures shows the basic cycle of actions in the model. The water authority has certain preferences and make decisions on investments in maintenance and new infrastructure that affects flooding and water scarcity. The agents’ decisions are like a basic decision tree. Although there is one water authority, there are different divisions who make decisions. Given the investment decisions, and the actual events, we get information on vulnerabilities. This could lead to adaptation or protests by neighborhoods.
Basically we try to include an endogenous dynamic of the political economy on investment decisions.

25. Example of Water Authority action: Areas for water extraction
Blue areas: Low values to take actions
Red areas: High level of social pressure (mobilizations)
Interactive use with stakeholders.
Evaluation of spatial explicit vulnerabilities.
Visualize the long term consequences of
certain priorities.
Connect with climate and hydrological
models.
In this slide we show a screenshot of the model that is under development. We have still need to derive some additional data to have an operational model, but we have had some first demonstration of the basic scope of the model with the water authority last month. We also derive feedback from neighborhoods on the assumptions we make on the actions of neighborhoods. We developed a game mimicking the model, and next we start a series of games with various neighborhoods to get feedback.

26. Number of changes made by the water authority in each census block and the level of water scarcity after 20 years
Expected Level of Scarcity
Expected Level of Investment
In this slide some initial results are provided. They show the spatial distribution of investments to cope with water distribution (focused on the center of Mexico city) and the expected level of scarcity (typically in the suburbs).

27. Discussion
Much of the social science is qualitative and describe theories in narratives.
From narratives to algorithmic statements require subjective interpretations.
Models useful to explore sensitivity to alternative theories and implementations of theories.
Substantial time investment is needed to bridge the two modeling cultures.
To conclude my experience in working across the aisles is that you need to understand the culture of the other disciplines. Social sciences have very diverse subfields and many of them are not appreciative of quantitative methods. A broader collaboration between social scientists and earth system scientists is needed to address the challenges we face. This require improved skills of collaborative work in large teams and using quantitative methods for the social scientists. But it also requires earth system scientists to provide social scientists space to find ways for them to get involved. It is not uncommon for interdisciplinary projects that social scientists are an afterthought without much incentives for them to be involved. We may have to develop new creative ways to integrate the qualitative knowledge of much of the social sciences in computational models linked to earth system processes, and may explore new ways how to interact with models and model results.

29. Appendix

30. Use for applications in human-environmental interactions unclear.
Big Data
Big data and machine learning are powerful to extrapolate behavior from recent clicks.
Use for applications in human-environmental interactions unclear.

31. The rise of experimental approach
Various types of experiments (lab, online, in field) in diverse disciplines.
Increasing popularity in economics, psychology, sociology, political science, ..
Measuring trust, honesty, decision making under risk, altruism,

32. Example: Dictator game
Two individuals A & B. They do not know each identity and cannot communicate. They may never know they were matched with each other.
A gets 10 dollars and can give a share to person B. B cannot make a decision. What will person A do?

33. Dictator game
Player 1: 5.57
Player 2: 4.43

34. Markets stimulate fairness?
People in market economies are used to interact with strangers. Without trusting strangers you cannot use money (piece of paper or plastic).
Henrich et al. (2010) Science

37. Original study effect size versus replication effect size (correlation coefficients).
Original study effect size versus replication effect size (correlation coefficients). Diagonal line represents replication effect size equal to original effect size. Dotted line represents replication effect size of 0. Points below the dotted line were effects in the opposite direction of the original. Density plots are separated by significant (blue) and nonsignificant (red) effects.
Open Science Collaboration Science 2015;349:aac4716
Published by AAAS

39. Ecosystem of models
The coupling I did in my dissertation was a tight coupling, while there are existing examples of louse coupling where results from models of certain topics are inputs in other models such as the ecology of models used by IIASA. The coupling is loose since the coupling is not happening every time step. The social science models in this example are limited to traditional economics models as well demography.