The fundamental tenet of theory is the concept of “empiria“. Empiria refers to our observations. Based on observations, scientists and researchers posit a theory – it is part of scientific realism.
A scientific model is a causal explanation of how variables interact to produce a phenomenon, usually linearly organized. A model is a simplified map consisting of a few, primary variables that is gauged to have the most explanatory powers for the phenomenon being observed. We discussed Complex Physical Systems and Complex Adaptive Systems early on this chapter. It is relatively easier to map CPS to models than CAS, largely because models become very unwieldy as it starts to internalize more variables and if those variables have volumes of interaction between them. A simple analogy would be the use of multiple regression models: when you have a number of independent variables that interact strongly between each other, autocorrelation errors occur, and the model is not stable or does not have predictive value.
Research projects generally tend to either look at a case study or alternatively, they might describe a number of similar cases that are logically grouped together. Constructing a simple model that can be general and applied to many instances is difficult, if not impossible. Variables are subject to a researcher’s lack of understanding of the variable or the volatility of the variable. What further accentuates the problem is that the researcher misses on the interaction of how the variables play against one another and the resultant impact on the system. Thus, our understanding of our system can be done through some sort of model mechanics but, yet we share the common belief that the task of building out a model to provide all of the explanatory answers are difficult, if not impossible. Despite our understanding of our limitations of modeling, we still develop frameworks and artifact models because we sense in it a tool or set of indispensable tools to transmit the results of research to practical use cases. We boldly generalize our findings from empiria into general models that we hope will explain empiria best. And let us be mindful that it is possible – more so in the CAS systems than CPS that we might have multiple models that would fight over their explanatory powers simply because of the vagaries of uncertainty and stochastic variations.
Popper says: “Science does not rest upon rock-bottom. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles. The piles are driven down from above into the swamp, but not down to any natural or ‘given’ base; and when we cease our attempts to drive our piles into a deeper layer, it is not because we have reached firm ground. We simply stop when we are satisfied that they are firm enough to carry the structure, at least for the time being”. This leads to the satisficing solution: if a model can choose the least number of variables to explain the greatest amount of variations, the model is relatively better than other models that would select more variables to explain the same. In addition, there is always a cost-benefit analysis to be taken into consideration: if we add x number of variables to explain variation in the outcome but it is not meaningfully different than variables less than x, then one would want to fall back on the less-variable model because it is less costly to maintain.
Researchers must address three key elements in the model: time, variation and uncertainty. How do we craft a model which reflects the impact of time on the variables and the outcome? How to present variations in the model? Different variables might vary differently independent of one another. How do we present the deviation of the data in a parlance that allows us to make meaningful conclusions regarding the impact of the variations on the outcome? Finally, does the data that is being considered are actual or proxy data? Are the observations approximate? How do we thus draw the model to incorporate the fuzziness: would confidence intervals on the findings be good enough?
Two other equally other concepts in model design is important: Descriptive Modeling and Normative Modeling.
Descriptive models aim to explain the phenomenon. It is bounded by that goal and that goal only.
There are certain types of explanations that they fall back on: explain by looking at data from the past and attempting to draw a cause and effect relationship. If the researcher is able to draw a complete cause and effect relationship that meets the test of time and independent tests to replicate the results, then the causality turns into law for the limited use-case or the phenomenon being explained. Another explanation method is to draw upon context: explaining a phenomenon by looking at the function that the activity fulfills in its context. For example, a dog barks at a stranger to secure its territory and protect the home. The third and more interesting type of explanation is generally called intentional explanation: the variables work together to serve a specific purpose and the researcher determines that purpose and thus, reverse engineers the understanding of the phenomenon by understanding the purpose and how the variables conform to achieve that purpose.
This last element also leads us to thinking through the other method of modeling – namely, normative modeling. Normative modeling differs from descriptive modeling because the target is not to simply just gather facts to explain a phenomenon, but rather to figure out how to improve or change the phenomenon toward a desirable state. The challenge, as you might have already perceived, is that the subjective shadow looms high and long and the ultimate finding in what would be a normative model could essentially be a teleological representation or self-fulfilling prophecy of the researcher in action. While this is relatively more welcome in a descriptive world since subjectivism is diffused among a larger group that yields one solution, it is not the best in a normative world since variation of opinions that reflect biases can pose a problem.
How do we create a representative model of a phenomenon? First, we weigh if the phenomenon is to be understood as a mere explanation or to extend it to incorporate our normative spin on the phenomenon itself. It is often the case that we might have to craft different models and then weigh one against the other that best represents how the model can be explained. Some of the methods are fairly simple as in bringing diverse opinions to a table and then agreeing upon one specific model. The advantage of such an approach is that it provides a degree of objectivism in the model – at least in so far as it removes the divergent subjectivity that weaves into the various models. Other alternative is to do value analysis which is a mathematical method where the selection of the model is carried out in stages. You define the criteria of the selection and then the importance of the goal (if that be a normative model). Once all of the participants have a general agreement, then you have the makings of a model. The final method is to incorporate all all of the outliers and the data points in the phenomenon that the model seeks to explain and then offer a shared belief into those salient features in the model that would be best to apply to gain information of the phenomenon in a predictable manner.
There are various languages that are used for modeling:
Written Language refers to the natural language description of the model. If price of butter goes up, the quantity demanded of the butter will go down. Written language models can be used effectively to inform all of the other types of models that follow below. It often goes by the name of “qualitative” research, although we find that a bit limiting. Just a simple statement like – This model approximately reflects the behavior of people living in a dense environment …” could qualify as a written language model that seeks to shed light on the object being studied.
Icon Models refer to a pictorial representation and probably the earliest form of model making. It seeks to only qualify those contours or shapes or colors that are most interesting and relevant to the object being studied. The idea of icon models is to pictorially abstract the main elements to provide a working understanding of the object being studied.
Topological Models refer to how the variables are placed with respect to one another and thus helps in creating a classification or taxonomy of the model. Once can have logical trees, class trees, Venn diagrams, and other imaginative pictorial representation of fields to further shed light on the object being studied. In fact, pictorial representations must abide by constant scale, direction and placements. In other words, if the variables are placed on a different scale on different maps, it would be hard to draw logical conclusions by sight alone. In addition, if the placements are at different axis in different maps or have different vectors, it is hard to make comparisons and arrive at a shared consensus and a logical end result.
Arithmetic Models are what we generally fall back on most. The data is measured with an arithmetic scale. It is done via tables, equations or flow diagrams. The nice thing about arithmetic models is that you can show multiple dimensions which is not possible with other modeling languages. Hence, the robustness and the general applicability of such models are huge and thus is widely used as a key language to modeling.
Analogous Models refer to crafting explanations using the power of analogy. For example, when we talk about waves – we could be talking of light waves, radio waves, historical waves, etc. These metaphoric representations can be used to explain phenomenon, but at best, the explanatory power is nebulous, and it would be difficult to explain the variations and uncertainties between two analogous models. However, it still is used to transmit information quickly through verbal expressions like – “Similarly”, “Equivalently”, “Looks like ..” etc. In fact, extrapolation is a widely used method in modeling and we would ascertain this as part of the analogous model to a great extent. That is because we time-box the variables in the analogous model to one instance and the extrapolated model to another instance and we tie them up with mathematical equations.
The past was so simple. Life was so simple and good. Those were the good old days. How often have you heard these ruminations? It is fairly common! Surprisingly, as we forge a path into the future, these ruminations gather pace. We become nostalgic and we thus rake fear of the future. We attribute a good life to a simple life. But the simple life is measured against the past. In fact, our modus operandi is to chunk up the past into timeboxes and then surface all the positive elements. While that is an endeavor that might give us some respite from what is happening today, the fact is that the nostalgia is largely grounded in fiction. It would be foolish to recall the best elements and compare it to what we see emerging today which conflates good and bad. We are wired for survival: If we have survived into the present, it makes for a good argument perhaps that the conditions that led to our survival today can only be due to a constellation of good factors that far outweighed the bad. But when we look into the future rife with uncertainty, we create this rather dystopian world – a world of gloom and doom and then we wonder: why are we so stressed? Soon we engage in a vicious cycle of thought and our actions are governed by the thought. You have heard – Hope for the best and plan for the worst. Really? I would imagine that when one hopes for the best and the facts do not undermine the trend, would it not be better to hope for the best and plan for the best. It is true that things might not work out as planned but ought we to always build out models and frameworks to counter that possibility. We say that the world is complex and that the complexity forces us to establish certain heuristics to navigate the plenar forces of complexity. So let us understand what complexity is. What does it mean? And with our new understanding of complexity through the course of this chapter, would we perhaps arrive at a different mindset that speaks of optimism and innovation. We will certainly not settle that matter at the end of this chapter, but we hope that we will surface enough questions, so you can reflect upon where we are and where we are going in a more judicious manner – a manner grounded on facts and values. Let us now begin our journey!
The sky is blue. We hear this statement. It is a simple statement. There is a noun, a verb and an adjective. In the English-speaking world, we can only agree on what constitutes the “sky”. We might have a hard time defining it – Merriam Webster defines the sky as the upper atmosphere or expanse of space that constitutes an apparent great vault or arch over the earth. A five-year-old would point to the sky to define sky. Now how do we define blue. A primary color between green and violet. Is that how you think about blue or do you just arrive at an understanding of what that color means. Once again, a five-year-old would identify blue: she would not look at green and violet as constituent colors. The statement – The sky is blue – for the sake of argument is fairly simple!
However, if we say that the sky is a shade of blue, we introduce an element of ambiguity, don’t we? Is it dark blue, light blue, sky blue (so we get into recursive thinking), or some other property that is bluish but not quite blue. What has emerged thus is an element of complexity – a new variable that might be considered a slider on a scale. How we slide our understanding is determined by our experience, our perception or even our wishful thinking. The point being that complexity ceases to be purely an objective property. Rather it is an emergent property driven by our interpretation. Protagoras, an ancient Greek philosopher, says that the man is a measure of all things. What he is saying is that our lens of evaluation is purely predicated on our experiences in life. There is nothing that exists outside the boundaries of our experience. Now Socrates arrived at a different view – namely, he proved that certain elements are ordered in a manner that exists outside the boundaries of our experience. We will get back to this in later chapters. The point being that complexity is an emergent phenomenon that occurs due to our interpretation. Natural scientists will argue, like Socrates, that there are complex systems that exist despite our interpretations. And that is true as well. So how do we balance these opposing views at the same time: is that a sign of insanity? Well, that is a very complex question (excuse my pun) and so we need to further expand on the term Complexity.
In order to define complexity, let us now break this up a bit further. Complex systems have multiple variables: these variables interact with each other; these variables might be subject to interpretation in the human condition; if not, these variables interact in a manner to enable emergent properties which might have a life of its own. These complex systems might be decentralized and have information processing pathways outside the lens of science and human perception. The complex systems are malleable and adaptive.
Markets are complex institution. When we try to centralize the market, then we take a position that we feel we understand the complexity and thus can determine the outcomes in a certain way. Socialist governments have long tried to manage markets but have not been successful. Nobel winner, Frederich Hayek, has long argued that the markets are a result of spontaneous order and not design. It has multiple variables, significant information processing is underway at any given time in an active market, and the market adapts to the information processing mechanism. But there are winners and losers in a market as well. Why? Because each of them observes the market dynamics and arrive at different conclusions. Complexity does not follow a deterministic path. Neither does the market and we have lot of success and failures that suggest that to be the case.
Let us look at another example. Examples will probably give us an appreciation for the concept and this will be very important as we sped through the journey into the future.
Insect behavior is a case in point. Whether we look at bees or ants, it is a common fact that these insects have extremely complex systems despite the lack of sufficient instruments for survival for one bee or one ant. In 1705, Bernard Mandeville wrote a book called: Fable of the Bees. It was a poem. Here is a part of the poem. What Mandeville is clearly hinting at is the fact that there would be an innate failure to centralize complex systems like a bee hive. Rather, the complex systems emerge in a way to create innate systems that stabilize for success and survival in the long run.
A Spacious Hive well stock’d with Bees,
That lived in Luxury and Ease;
And yet as fam’d for Laws and Arms,
As yielding large and early Swarms;
Was counted the great Nursery
Of Sciences and Industry.
No Bees had better Government,
More Fickleness, or less Content.
They were not Slaves to Tyranny,
Nor ruled by wild Democracy;
But Kings, that could not wrong, because
Their Power was circumscrib’d by Laws.
Then we have the ant colonies. An ant is blind. Yet a colony has collective intelligence. The ants work together, despite individual shortcomings that challenge an individual survival, to figure out how to exist and propagate as group. How does a simple living organism that is subject to the whims and fancies of nature survive and seed every corner of the earth in great volumes? Entomologists and social scientists and biologists have tried to figure this out and have posited a lot of theories. The point is that complex systems are not bounded by our reason alone. The whole is greater than the sum of the parts.
A complex system is the result of the interaction of a network of variables that gives rise to collective behavior, information processing and self-learning and adaptive system that does not completely lie in the purview of human explanation.