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.
If you are in finance, you are a risk manager. Say what? Risk management! Imagine being the hub in a spoke of functional areas, each of which is embedded with a risk pattern that can vary over time. A sound finance manager would be someone who would be best able to keep pulse, and be able to support the decisions that can contain the risk. Thus, value management becomes critical: Weighing the consequence of a decision against the risk that the decision poses. Not cost management, but value management. And to make value management more concrete, we turn to cash impact or rather – the discounted value of future stream of cash that may or may not be a consequent to a decision. Companies carry risks. If not, a company will not offer any premiums in value to the market. They create competitive advantage – defined as sorting a sustained growth in free cash flow as the key metric that becomes the separator.
John Kay, an eminent strategist, had identified four sources of competitive advantage: Organization Architecture and Culture, Reputation, Innovation and Strategic Assets. All of these are inextricably intertwined, and must be aligned to service value in the company. The business value approach underpins the interrelationships best. And in so doing, scenario planning emerges as a sound machination to manage risks. Understanding the profit impact of a strategy, and the capability/initiative tie-in is one of the most crucial conversations that a good finance manager could encourage in a company. Product, market and internal capabilities become the anchor points in evolving discussions. Scenario planning thus emerges in context of trends and uncertainties: a trend in patterns may open up possibilities, the latter being in the domain of uncertainty.
There are multiple methods one could use in building scenarios and engaging in fruitful risk assessment.
1.Sensitivity Assessment: Evaluate decisions in the context of the strategy’s reliance on the resilience of business conditions. Assess the various conditions in a scenario or mutually exclusive scenarios, assess a probabilistic guesstimate on success factors, and then offer simple solutions. This assessment tends to be heuristic oriented and excellent when one is dealing with few specific decisions to be made. There is an elevated sense of clarity with regard to the business conditions that may present itself. And this is most commonly used, but does not thwart the more realistic conditions where clarity is obfuscated and muddy.
2.Strategy Evaluation: Use scenarios to test a strategy by throwing a layer of interaction complexity. To the extent you can disaggregate the complexity, the evaluation of a strategy is better tenable. But once again, disaggregation has its downsides. We don’t operate in a vacuum. It is the aggregation, and negotiating through this aggregation effectively is where the real value is. You may have heard of the Mckinsey MECE (Mutually Exclusive; Comprehensively Exhaustive) methodology where strategic thrusts are disaggregated and contained within a narrow framework. The idea is that if one does that enough, one has an untrammeled confidence in choosing one initiative over another. That is true again in some cases, but my belief is that the world operates at a more synthetic level than pure analytic. We resort to analytics since it is too damned hard to synthesize, and be able to agree on an optimal solution. I am not creaming analytics; I am only suggesting that there is some possibility that a false hypothesis is accepted and a true one rejected. Thus analytics is an important tool, but must be weighed along with the synthetic tradition.
3.Synthetic Development: By far the most interesting and perhaps the most controversial with glint of academic and theoretical monstrosities included – this represents developing and broadcasting all scenarios equally weighed, and grouping interaction of scenarios. Thus, if introducing a multi-million dollar initiative in untested waters is a decision you have to weigh, one must go through the first two methods, and then review the final outcome against peripheral factors that were not introduced initially. A simple statement or realization like – The competition for Southwest is the Greyhound bus – could significantly alter the expanse of the strategy.
If you think of the new world of finance being nothing more than crunching numbers … stop and think again. Yes …crunching those numbers play a big part, less a cause than an effect of the mental model that you appropriate in this prized profession.