Before we begin I want to let the class know that the success of this class will depend greatly on our ability to think and communicate our thoughts to each other. Many of the topics we will be discussing are constantly debated within the policy and scientific arenas. The purpose of this course is to improve our understanding and communication of models and model predictions within a decision-making context. I have continually updated this course over the past 3 years and it is still a work in progress. With that said, what you learn and take away from this class will be largely based on what you are willing to put into this course It will depend onbe largely based on what you are willing to put into this course. It will depend on your willingness to criticize the way you and our community think, and you will need to question yourself and each other (as a side note: I am included in this).

So now lets start to look at predictions and models.

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To start this class off I would like to begin with a story that is used by Dr. Haefner in his modeling course at Utah State University.

There are six blind men who are asked to inspect an elephant. They are asked to identify the object before them which they can not see. One man, feeling the elephant’s leg, thinks he is touching a tree trunk. Another, grasping the elephant’s trunk, thinks he is holding a large snake. A third, standing near the moving ear, thinks it is a large feathered fan. And so it goes for the other men touching the tusk, the side and the tail of the elephant. Each man gives a different description of the same object, but none are correct.

Three fundamental lessons can be gleaned from this simple story. First, in the real world, we do not know it is an elephant: there are no omniscient observers with pspecial access to the truth. Second, all men collected basic data and generated a hypothesis consistent with the data. This activity, which is distinct from deduction or induction is called abduction (we will define these in one minute). Third, abduction is infallible.

As another example look at the two pictures you see here and describe what you

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p p y yare looking at. If you are like most people (according to Dr. Ford), you will conclude that a white triangle has to be superimposed over a black triangle and three black circles – it’s the simplest way to explain the entire drawing in a consistent manner. When asked about the cartoon, most people respond that the curved line is a circle. They usually follow this with some theory about someone playing a trick on the person by cutting the floor out below them and the best way to do this would be to cut a circle This type of discussion brings up an interesting point that we all have

In logic theory there are three kinds of reasoning: abduction, deduction and i d ti S h t d th ?induction. So what do these mean?

Deduction is when we determine the conclusion based on a rule and its precondition. Induction is when we determine the rule after numerous examples of the conclusion have been viewed given a precondition. Abduction is when we determine the precondition using a conclusion and a rule. In other words we assume precondition could explain the conclusion.

To illustrate these concepts I will use a few examples from Wikipedia(http://en.wikipedia.org/wiki/Logical_reasoning). In the deductive type of reasoning we might make the following statement: “When it rains the grass gets wet.” This is our rule and its precondition.our rule and its precondition.“It rains, thus the grass is wet.” This is our conclusion based on the above rule and precondition. This type of reasoning is commonly associated with mathematics. A scientist or statistician, however, often uses inductive reasoning. In this case the conclusion is measured from the precondition (“every time it rains the grass is wet”) and a rule is developed (“when it rains the grass gets wet”). So abduction would combine the rule “when it rains, the grass gets wet” with the conclusion of “the grass is wet” to determine the precondition of “it must have rained”. This type of logic can be viewed in any episode of “Law and Order” or any other detective show on TV.

These concepts are important to think about because they will help us identify types of models as well as provide insight into what assumptions might have been made. For example in both abductive and deductive logic we must assume the rule to be

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So now that we have some ideas about logical reasoning let’s talk about models. A model is a substitute for a real system. In natural resources the system we are substituting for is nature. Therefore, a model is a way to look at nature. It allows us to formally or informally represent our understanding of the natural world. Many of us have heard the phrase “all models are wrong; some are useful”. This is a true statement because a model is not real; it is a substitute, therefore it can never represent the real system in which it was intended to model.

Some examples of common models used in fuels management include fire behavior prediction fuel models, Rothermel’s surface fire spread model, and the forest vegetation simulator. We will talk more about classifying models in the next lesson so until then just know that these are all models.

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Models are an important aspect of the sciences. From physics and engineering to agriculture and natural resources, models are utilized to help organize data, synthesize information and make predictions. Models can range from simple regression equations (such as canopy biomass equations) to analytical models and complex numerical simulations. Traditionally models like the ones shown in the two pictures here are what scientists used for centuries. We probably all remember using the sticks and balls in chemistry class to represent different compounds. However with the development of computers models have transformed from theseHowever, with the development of computers, models have transformed from these traditional visual concepts to well thought-out complex mathematical formulations of the natural world.

For the purpose of this class we will review the use of models to make predictions on which management decisions will be based. This leads us into the title of this course: “Scientific fuels management planning” or in other words “using science tocourse: Scientific fuels management planning , or in other words, using science to improve our decision-making”. We could use science in many ways to help us make better decisions; however, in this class we will focus on using scientific predictions to make better decisions.

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So I have already given you a hint that some models are used for making predictions, but what are the other models used for? The first use of a model is to provide an understanding of a system’s theory. This type of model is related to inductive logic. In other words, we use the inputs and conclusions to develop a theory or understanding of the system. The next type of model is developed to make predictions, and these are often called analytical models. These models use a precondition (input) and a rule (our

d t di f th t ) t di t th l i f th tunderstanding of the system) to predict the response or conclusion of the system. This type of model is similar to deductive logic. The last type of model we could develop is a instrumentation or control type of model. These models are often used by engineers to develop a system in a way that gives a specific output given a input. It is similar to abductive logic in that we rely on some understanding of a system and a outcome of that system to develop the inputsthe inputs.

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Models can be used to solve each of the problem areas we just talked about (synthesis, analysis and instrumentation) to allow us to understand, predict and control systems. However, there are also several important secondary uses of models that are derived from the social uses of science. In particular we can use models to develop a conceptual framework such as designing experiments or allocating research dollars. Or we can use them to summarize large quantities of data; for example, we could combine multiple data sets together to help improve our understanding We could also use models to identify areas of ignorance; forunderstanding. We could also use models to identify areas of ignorance; for example, we might look at a model of emissions and ask how different types of rotten logs are affecting emissions. We could also use models to help provide insight for managers and planners. For example, we could use the forest vegetation simulator to run “what if”-type scanarios when planning a fuels treatment. This concept is very important for us in this class as it will tie together the dual roles of modeling and planning.

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Let’s now attempt to formalize and understand the relationships among modeling d t d l i T d thi fi t t t k l k t th th fand management and planning. To do this we first must take a look at the theory of

science. Let’s first start by talking about the history of science (for a good book discussing the history of predictions get “The Future of Everything” by Orrell). The earliest ideas in many fields of science was that we observe the world and then make up theories that fit our observations. Thus it was very much based on inductive logic. There are many famous scientists who have made great contributions using this method. A few examples include William Whewell (1794-1866) who introduced calculus to British audiences and Charles Lyell (1797 1875)1866) who introduced calculus to British audiences, and Charles Lyell (1797-1875) who published some of the most classic geology books promoting the idea that science could and should be formulated by first observing and then forming a theory. The importance of Lyell as a scientist can be found on his influence on Charles Darwin (who actually had a copy of Lyell’s recently published books with him on the H.M.S. Beagle). In Darwins “Origin of Species” and “The Descent of Man” are 1000+ pages which use examples to support his theory. The bottom line was that these works were using inductive reasoning - they were not predictivewas that these works were using inductive reasoning they were not predictive theories.

Later in the 20th century a change occurred in how scientists and philosophers thought about science. This method (often called the scientific method) proclaimed that scientific inquiry proceeds by formulating a hypothesis in a form that could conceivably be falsified by a test on observable data. This new type of scientific inquiry was based around the philosophical notion that there were problems withinquiry was based around the philosophical notion that there were problems with inductive reasoning. Serwitz et al ( 2000) summarize this shift:“Empiricism “ [the idea that we can make generalizations based on many observations] “is an insufficient philosophy for understanding the development of scientific knowledge, because life is a sea of experiences and perception. So how do we decide which ones to take note of?” In other words this new thinking suggests that, by first focusing on the observations, 8

Based on our current scientific thought (the ‘hypothetico-deductive model) the principle task of science is to generate hypotheses, theories or laws and compare them with experiences and observations. The problem with this (as many have realized) is that it works reliably only if we are dealing with closed systems. Remember that in this thought pattern, our conclusion can only be correct if our rule is a complete description of the system (or if the system is a closed system). In reality no system is completely closed, but systems do have different levels of openness Therefore in natural systems there are always external contingenciesopenness. Therefore, in natural systems, there are always external contingencies that may not be fully specified or even known. So when we build a model we must make assumptions about these other factors, such as completely frictionless surfaces, perfect predators, or homogeneous fuels across a stand. Another way to think about this concept is to compare a natural system to an artificial system.

So let’s first consider arithmetic We can be confident that if 2 + 2 = 4 then 4-2 =2So let s first consider arithmetic. We can be confident that if 2 + 2 = 4, then 4-2 =2, because we have defined the terms this way and because no other factors are relevant to the problem. Now let’s consider the question: “Is a straight line the shortest distance between two points?” The short answer for most of us is “Yes”, and this is what we would have gotten credit for on a geometry test. But now consider a natural system where we have three dimensions. In fact the shortest distance may in fact be a large circle.

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As I just mentioned, all models are open, their conclusions are not true by the virtue f th d fi iti f th t d th l t t With thi i i d itof the definition of the terms, and they only represent a system. With this in mind it

is easy to see how no model completely encompasses any natural system, and by definition a model is a simplification. We simplify the natural systems so that we can make them tractable. This leads us to the question of how models might be considered open. First, a model can be considered open with respect to its conceptualization. For example, when we create a model, we abstract from the natural world certain elements that we believe to be important to the problem and omit all other factors It is these other factors that are omitted which make theomit all other factors. It is these other factors that are omitted which make the model open. There are many models in science that have omitted certain factors which latter were found to be important. In other cases we might want to include a factor but cannot due to other reasons (money and time are big ones).

Models can also be open based on the adequacy of the governing equations. There is a great deal of philosophy that goes into this argument, but the bottom line is that we really have no way of knowing how well our laws or governing equations actually represent the system. They are simply our best approximations of what is going on in the system.

Finally models may be open with respect to the inputs. This idea is a bit tricky. On one hand we know what the variables are but on the other we have to make choices about how to best represent those variables. For example, we know that canopyabout how to best represent those variables. For example, we know that canopy bulk density is important tin predicting crown fire spread, but how do we best represent this value? Do we use a mean? The highest value? The lowest value? All models are open for one or more of these reasons. The more we do not know about a system the more open it is. However, be aware that understanding a model is open is not saying it is bad. It is just acknowledging this idea.

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The whole idea or concept with decision making is to look into the future and predict what is going to occur, typically to either prevent it from happening or to be prepared for what will happen (more on this later). In essence making a decision is a forward-looking process: we look into the future, formulate alternative actions, and select among them based on our expectations of how things will turn out. It is with this idea that we can begin to see why the predictive capacity of science holds great appeal for policy makers and decision makers. This type of logic has lead some to suggest that predictions can ultimately replace or become a substitute for politicalsuggest that predictions can ultimately replace or become a substitute for political and moral decisions involved in policy-making.

Predictions serve two goals in our society: they test scientific understanding, and they serve as a potential guide for decision-making. It is this second goal that we are most concerned with as land managers. The question we need to consider is if the use of a prediction (which proves a scientific theory) is adequate for dictatingthe use of a prediction (which proves a scientific theory) is adequate for dictating public policy. Or in other words, just because a prediction is scientifically valid, does that mean it is equally valid for management and decision-making?

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Sarewitz et al (2000) have identified 6 major concerns that must be considered in evaluating the capacity of predictive research to contribute to positive policy outcomes.

The first concern with using a prediction in policy is that the phenomena or process we are trying to predict may not be easily predicted across useful geographic or time scales. As an example of this, we can think about the inability of science to predict earthquakes. A brief review of this process has lead from scientists attempting to predict the occurrence of a earthquake to providing a warning to distant areas after one occurs. As a note, although there are many examples of the predictions themselves failing in this case, policy and public awareness was affected by these and has lead to such changes as improved building codes.

The second concern which must be considered is that accurate predictions of phenomena may not be necessary to respond effectively to political or socioeconomic problems. This concern could be tied back into the earthquake example we were just discussing, or you could think of floods or hurricanes as well. In all cases building codes and engineering methods can be applied to reduce the damage, without needing an accurate prediction of when and where the event will occuroccur.

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The third concern which must be considered, is the idea that necessary or feasible liti l ti b d f d i ti i ti f di ti i f ti hi h ipolitical action may be deferred in anticipation of predictive information, which in

many cases may or may not be coming soon. Sarewitz et al use the example of global climate change here, but let’s use another example. To help illustrate this concept I would like to talk about long-term nuclear waste disposal. This is a topic that is personal to me as I have studied in nuclear engineering and remember many course lectures which dealt with how to handle nuclear waste (many of them were not very fun, and involved large amounts of math). The story shows how actions may be delayed due to an anticipation of nuclear waste Beginning in the 1940s themay be delayed due to an anticipation of nuclear waste. Beginning in the 1940s the production of plutonium and the eventual production of nuclear reactors would began to create large amounts of high-level radioactive waste, and as early as 1957 a committee had advocated that the best way to store the waste was in a large deeply-mined geologic repository. The process of selecting a site was underway and in 1986 the list was down to 3 sites. Some 30 years after the original report a site was still not selected due to a lack of low-uncertainty scientific predictions, though much effort was put into these predictions. By 1987 the Yucca Mountain sitethough much effort was put into these predictions. By 1987 the Yucca Mountain site was selected. Since this time studies have been underway, predictions made and remade with newer science and the open date is now scheduled as March 31, 2017,although much opposition still exists. The decisions depended on an accurate prediction of percolation flux rate, which in the course of about five years had changed by as much as a factor of forty. The inability for us to predict the flux rates will ultimately mean 70 years will have passed where policy makers did not make any contributions to the issue of long-term storage of nuclear waste (I might be a bit harsh here but you would think in 70 years something constructive would have been done).

The next concern to consider is that predictive information might be subject to misuse and manipulation. As an example of this concern Moran (2000) cites the EIS report filed by the BLM for the Aguirre mine as a case where the model was used not as a tool but instead as a way to provide certainty to the predictions and to

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The last two concerns that they have identified are that the criteria for scientific success in prediction may be different from criteria for policy success, and that an emphasis on predictive sciences moves both financial and intellectual resources away from other types of scientific activities such as monitoring and assessment. The last concern is related to the idea that we will wait for a prediction instead of monitoring and assessing the problem. The former concern I have listed here can be exemplified by the appraisal for oil and gas over the last half a century.

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Let’s now return our focus to modeling of natural systems. In general there are three approaches used to building quantitative models (we will cover these more in the next lecture but I want to introduce this idea now). The first is an informal model, and we use these constantly to interpret the world around us, often without realizing that we are doing so. These types of models are often deeply-ingrained assumptions, generalizations, or even pictures and images that influence how we understand the world and how we take action (Senge 1990). The second type of model is the mathematical characterization For example surface fire spread can bemodel is the mathematical characterization. For example, surface fire spread can be modeled using mathematical principles. We will talk more about this in the next lesson but oftentimes these types of models are said to be based on first principles. Models built upon first principles attempt to explain the processes which are governing the event being predicted. Think physics here!

Other times we may look for a statistical link between two variables These types ofOther times we may look for a statistical link between two variables. These types of models do not attempt to explain why an event is happing like mathematical models but instead rely on statistical relationships to predict a future occurrence.

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We have now painted a picture where the very idea that we can use a model to help make a decision is problematic. To begin with, a model is an abstraction of reality; it has inherent uncertainty in it. We will talk more about uncertainty later in this class, but for now let’s leave it at “all models have some uncertainty”. We have also looked at why decision-makers seem to encourage the use of models and their inherent uncertainties.

Decision-makers and analysts have often suggested that the effective link between science and policy depends upon two factors. First, that uncertainties in model predictions must be reduced, and second, that individuals must effectively communicate the nature and magnitude of those uncertainties to the people who must take action.

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As we move through this course I would like to think about the following questions.

If a model is a simplification of nature and therefore can never fully capture the range of behavior of the real system, how wrong can a model be and still be useful in a management context?

How do we effectively communicate the nature and uncertainties of modelHow do we effectively communicate the nature and uncertainties of model predictions?

Most importantly I want you to improve your understanding of model predictions and have fun. This class is going require critical thinking about these issues; there are no right answers or bad questions in this class. As you will see in the readings, man scientists are debating these same iss es This is o r chance to oice o rmany scientists are debating these same issues. This is our chance to voice our opinions and thoughts.

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