I recently borrowed an academic book from a local university called Agent-Based Modeling of Environmental Conflict and Cooperation (2019) by Todd K. Bendor and Jurgen Sheffran. The cover image depicts a place 20 km from Cape Town South Africa with poor housing and services on the left, rich gated housing on the right and no man's swamp land in the middle.

The main reason I picked up the book was because it looked like it had some useful applications of systems thinking to an important topic: modelling conflicts to help resolve conflicts. I like the fact that it is not an edited volume and provides a consistent perspective and level of quality on a variety of interesting topics in conflict modelling and conflict resolution. There is some math in the book, which I also like, because it is used to clarify and add precision to important ideas, not to prove theorems and other niceties. There are some practical agent-based modelling applications discussed in Part III of the book which is where a good chunk of value of this book lies in my opinion.

Table of Contents

Part I: Conflict and the Promise of Conflict Modeling

1. Environmental Conflicts in a Complex World

2. Why Model? How Can Modeling Help Resolve Conflict?

3. The History and Types of Conflict Modeling

4. Participatory Modeling and Conflict Resolution

Part II: Modeling Environmental Conflict

5. System Dynamics and Conflict Modeling

6. Agent-Based Modeling and Environmental Conflict

7. Modeling Conflict and Cooperation as Agent Action and Interaction

Part III: Applications of the VIABLE Model Framework

8. A Viability Approach to Understanding Fishery Conflict and Cooperation

9. An Adaptive Dynamic Model of Emissions Trading

10. Modeling Bioenergy and Land Use Conflict

11. The Future of Modeling Environmental Conflict and Cooperation

You don't have to read the book cover to cover to get something out of it. To maximize your time, you can scan through and find an application or section that interests you and read about that. For example, I came across a section called The Farmer Agent Model on pp. 292 to 295 and decided that it might be worth investing some time into that concept. One component of the The Farmer Agent Model is the harvest function which I have simplified to:

h = r * A * B * f

The harvest for a particular crop h by a particular farmer is found by multiplying:

• r: The fraction of land planted in that crop, also called the priority of the crop.
• A: The arable land area in hectares used for the crop.
• B: The biomass yield per hectacre.
• f: The fraction of biomass produced that is harvested (e.g., 90%)

If you add up the harvest amount for each farmer h harvesting that crop sum(h), then you get the total harvest for that crop H. You can use this sum as the crop supply in agricultural equations of supply and demand to figure out the price you might get for that crop. If prices are low for a particular crop then The Farmer Agent Model might respond by adjusting the value of the crop priority r downward so less land area is devoted to that crop. Using harvest functions, pricing functions, and investments functions in an integrated model allows you to tweak parameters to see how they influence other parts of the overall model. Government investment in ethanol production using subsidies, for example, will increase the value of r for bioenergy crops.

Another interesting topic in Chapter 10: Modelling Bioenergy and Land Use Conflict involves using spatial models of farmers across the landscape so that you take into account spatial interactions among farmers. We don't just treat farmers as disembodied harvest functions, but make an effort to locate those harvest functions (aka farmers) on a grid so you can simulate and explore local interactions that might be important to account for.

Conflicts can arise in communities establishing a bioenergy plant because citizens may have differing views on fuel, water, land use changes that the plants bring with them or signify. Some may object to the increased water use or the nitrogen runoff that might accompany increased corn production next to streams. The other side may object that farmers need to be profitable and the plant jobs are needed. Many of these issues and interactions may be left unmanaged if they are not explicitly modelled as part of an overall model of the bioenergy plant and how it interacts with the ecosystem, the main stakeholders, and the government incentives that might be driving increased production of bioenergy crops.

In Illinois they have some of the best soil and growing conditions for bioenergy crops and other types of crops so bioenergy conflicts are likely more common there. Where I live, there is a conflict around burning wood for energy in a local paper mill. The pro side argues that it helps the mill keep their costs of operations lower because power is a huge cost for them, that we don't have to make expensive upgrades to our power grid to supply the Mill with power, that burning wood for energy is better than burning coal because the wood is renewable, and that it is helping sustain the economy with forestry employment in rural areas and good paying mill jobs. The opposing side argues that the large number of truckloads of wood a day needed to feed the bioenergy plant is not green, that it is decimating forests and habitat, that we can find better uses, that we can be adding more value to our products and keep the same level of jobs and economic prosperity, etc..

Fisheries is a big industry here and there are lots of conflicts between fishermen and the government over quotas, between fishermen and environmental groups over the cause of whale deaths and what needs to be done, between fishermen and another local pulp and paper mill over the mills desire to pipe effluent into fishing grounds. The powerful pulp and paper industry has met its match when their practices affect the equally powerful fishing industry.

Because I have an interest in fisheries (my father-in-law and his sons are fishermen) I was interested in picking up this book to see how they approach modelling conflict in the fisheries (see Chapter 8. A Viability Approach to Understanding Fishery Conflict and Cooperation). The starting point for all the models we might want to create is generally is a simple stock and flow diagram for a single fish population. From here we start drawing other boxes and linkages to capture more of the complexity of the situation.

Agent based modelling often starts with creating visual depictions of the system. Stock and flow diagrams are a commonly used technique but you generally need to master a few more visualization techniques to depict a full systems model. The best intro to these techniques is Thinking in Systems (2008) by Donnella H. Meadows.

The German philosopher Gottfried Leibniz (died 1716) was famous for his slogan "Let Us Calculate". The slogan conveyed his belief that conflicts could be resolved by representing problems in a logical calculus that would help reasonable people find solutions. The belief that we can resolve conflicts through modelling therefore is not a new idea. What is new is that the agent based models (aka "logical calculus") are getting better at incorporating more of the complexity of the situation and providing a better decision aid for resolving conflicts. Leibniz's envisioned logical calculus can be implemented in the multi-agent programmable modeling environment called NetLogo which was used as the agent-based modelling platform for the conflict models discussed in this book. I've toyed with the idea of learning NetLogo, but this book gives me more reasons to do so as the book would be of even greater value if I downloaded and ran their conflict models.

It should be noted, however, that resolving conflicts is often not as simple as coming up with a good conflict model and the value of modelling can be overrated when it is done poorly. It tends towards armchair theorizing if not communicated to and validated by stakeholders in the conflict. That being said, doing armchair theorizing based on NetLogo is better than doing armchair theorizing based on NetFlix.

This is a very limited review of the book to give you a flavor of the content you might find. Not exactly a coffee table book or one that would appeal to a wide audience, or one that is accessible price wise; nevertheless I think it is worth seeking out for those with an interest in exploring the use of agent based models to represent and resolve conflict situations. Also, anyone wanting to add a new book to their systems thinking collection.

This morning I started reading a research paper by MIT researchers César A. Hidalgo and Lisa E. Castañer called Do we need another coffee house? The amenity space and the evolution of neighborhoods (2015, PDF Link). The concept of an Amenity Space is a potentially powerful concept as it can be used to recommend what amenities might be missing, or not, in a neighborhood. The concept leads to a potential discovery technique for entrepreneurs and investors to find new businesses that might be viable in a given neighborhood.

One of the authors, Cesar Hidalgo, recently wrote a book called Why Information Grows: The Evolution of Order, from Atoms to Economies (2015) that looked like it might be interesting.

I decided to read the article to get some exposure to Cesar's thinking and because I was exploring the Atlas for Economic Complexity and realized he was also a driving force behind that impressive visualization project.

In this article, the authors used Google Maps to gather data on the amenities available in various neighborhoods. They computed the correlations between the amenities (how strongly the presence of one amenity predicted another) and represented the strength of these correlations by the thickness of the links between the amenity nodes. You can read more about how the visualization was constructed at the bottom of the diagram.

The paper proposes an algorithm that generates recommendations for new amenities for a neighborhood. I recommend you read the paper if you want these details.

The concept of an amenity space might be useful not just for making recommendations for new neighborhood amenities but for explaining why certain destinations are good tourist destinations, why real estate prices are higher in certain neighborhoods, what cities might do to improve their neighborhoods, etc....

Another point the authors make in the paper is that neighborhoods often have a pattern of specialization (e.g., tourism amenities) which might suggest a particular type of amenity that would be a good fit. An example from my own experience is the relationship between world-class golf courses and the need for nearby airport facilities for private jets. One tourist amenity (world-class golf courses) drives the need for a complementary amenity (airport for private jets to land). High-end golfers apparently also like high-quality whiskey and a nearby single-malt whiskey distillery with lounge and lodgings benefited significantly from the arrival of the golf course to the neighborhood. The golf courses are driving a new evolution of amenities in this rural community. While the concept of amenity space was developed based upon business co-location data in larger cities, it might be useful for thinking about expected co-location patterns in rural areas as well.

It also appears that the recommender system might take the form of a lens model as linear regression techniques were used to make the recommendations.

I finished up Sim Van der Ryn and Stuart Cohen's book Ecological Design (2007, Tenth Anniversary Edition).

Overall I found it a worthwhile read. It is a well crafted discussion on what ecological design is and what its main principles are. Many of the ideas in this book have been assimilated into mainstream sustainability thinking so the ideas are probably not as novel as they were when first articulated. Something that has started to sink in after reading the book is the idea that ecological design informs not just how we do building, landscape and urban design, but can also inform everyday decision making such as how to source food for your family and what type of soap to buy. It is when we try to apply some of these principles outside of the usual built environment context that things become more challenging and interesting.

According to Sim and Stuart, the 5 main principles of ecological design are:

1. Solutions Grow from Place
2. Ecological Accounting Informs Design
3. Design with Nature
4. Everyone is a Designer
5. Make Nature Visible

In today's blog I wanted focus on the second principle, that "Ecological Accounting Informs Design". I am a bit hesitant to discuss this principle for a couple of reasons. One reason is that I'm not much of an accountant (I do, however, prepare and file my own personal taxes) so can't draw upon a deep knowledge of the accounting field. This would be useful. The second reason that I am hesitant is because it is challenging to understand how ecological accounting might be implemented in a practical way. I've decided to tackle this challenge today and see where it goes.

The basics of ecological accounting involve setting up accounts for the different types of "impacts" you want to manage - soil, water, energy, pollution, biodiversity, etc... You can already see that putting such a system into practice might be difficult. Estimating and measuring all the different types of ecological impacts associated with your design is not a trivial undertaking. In practice, we might only be worried about a few of the most obvious impacts. If we limit our scope, the prospect of performing an ecological accounting becomes more realistic. We can always add another dimension when we have management of the obvious impacts under control.

Ecological accounting comes into play when we are evaluating design alternatives. There is not much point of doing ecological accounting on a product or process just for the sake of doing it. Ecological accounting is useful for evaluating whether you should go with one design alternative over another because it minimizes some impacts (e.g., co2 emissions) or maximizes some benefits (e.g., improves soil) which collectively suggest which design is the most appropriate.

One important dimension to include in your accounting is the cost dimension. You may have a great ecological design but what happens when you discover that it costs alot more than a less ecological design? How do we manage the tradeoffs? How do we equate costs and levels of pollutants? Is one more ton of co2 produced equal to -$44 USD in your balance of accounts?

Putting a price tag on ecological services is a big field these days. The Natural Capital Project has a huge number of Ph.D researches developing software to estimate the value of the many ecological services that nature provides.

Ecological accounting can also be viewed as a way to engage in design that specifically involves 1) life-cycle analysis and 2) following the flow.

A life-cycle analysis involves an examination of impacts over time and encourages us to examine what happens to the design after it has served its useful life. What will become of it? If one design generates landfill waste and another involves composting, then the latter design is to be preferred.

Follow the flows means understanding the material flows, energy flows, transportation flows, pollution flows, heat flows, and water flows that are required for one design versus another.

Following the flows encourages us to ask questions such as:

• Where do the materials for the product come from?
• How much transportation is required?
• How much energy will be used at each stage of production?
• How will waste streams be managed?
• How will water usage be managed?
• How will heating be managed?

When we follow the main flows we have better ecological accounting to use for evaluating our design alternatives.

How do we find designs that have the best ecological accounting?

One approach would have us carefully evaluating our design with respect to all our ecological accounts and picking the design with the best ecological accounting score. I don't think the process of ecological design is or has to be this rational to succeed. Instead it can rely upon heuristics that while not guaranteed to produce good ecological accounting scores often end up doing so.

Here I enter uncharted territory. I'll propose a couple of practical heuristics for your consideration:

1. Do it cheaply. Within reason.
2. Apply zonation.

The heuristic that you should do it cheaply forces you to act under resource constraints that often produces a design with good, and sometimes optimal, ecological accounting. The "within reason" part is a reminder that if you are too cheap you may sacrifice quality to an unacceptable extent. You might have to back off from the cheapest design for this reason but finding the cheapest design is a worthwhile design activity to engage in to find a design with good ecological accounting to begin with. You can back off from there to optimize on other dimensions that are important to you besides cost.

The second heuristic that will often lead us to finding a design with the best ecological accounting is to apply zonation. Zonation is a concept from Permaculture and is generally used to design the layout of a farm in a way that minimizes travel time based upon how frequently we have to visit different parts of the farm. So put your garden, which you must attend to everyday, in zone 1, which is next to your home, which is in zone 0. Put the apple trees which you need to tend to visit less in zone 3 or 4, just within the zone 5 which is your wild zone which you need to visit even less frequently.

Toby Hemmenway has been creatively applying zonation to many different areas from transportation to foodsheds. His zonation of foodsheds has resulted in a foodshed design that arguably has better ecological accounting along many dimensions than our current foodshed design. This is a diagram from Toby's new book, The Permaculture City (2015), which is also about ecological design but from a more permaculturally inspired perspective.

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