 One of the great achievements of human creativity has been the development of abstract representations of the world around us. Indeed, this capacity for abstract thought is a characteristic of modern humans, and it has much of its origins in the development of language as a way of symbolizing and communicating abstract concepts. This capacity for abstract modeling forms the foundations to our advanced civilization and has extended the human ability to much better understand the world. Communicate that understanding between each other and to collaborate around these shared models. A model is an abstract, typically compact representation of some phenomena that enables us to conceptualize and communicate its basic structure and dynamics in a coherent form. A central characteristic of models and modeling of all kind is the use of abstraction, whereby various levels of detail are removed from the original empirical phenomena in order to create a compact schema or diagram of the system under consideration. Models help us organize and structure information, clarify our reasoning, communicate, solve problems and predict future events. Abstraction is the process of considering something independent of its associated attributes. The use of abstraction involves removing successive layers of detail from a representation in order to capture only the essential features that are generic to all entities of that kind, independent of their specific form. Abstraction involves the use of inductive reasoning, that is to say, identifying common attributes to a variety of instances of some entity. And the formation of a generic model that captures the fundamental aspects of it without reference to any specific instance or context. An algebraic equation, an architect's master plan of a building or a non-figurative painting would be good examples of abstract representations. They all have in common the aim of capturing and communicating only the most essential features to the system they're representing by removing specific concrete details of their form or function. All models are simplified representations of reality. Models require the compression of information or data to generate a simplified form. This simplification process is essential for grasping the whole of the system. Most systems in the real world are far too complex for us to grasp as a whole due to their many diverse parts, interactions and scale. In practice, we can typically only interact with and experience a small subset of a system. Abstraction helps us to synthesize our many experiences of some entity into a coherent impression of the whole. For example, one may have a model as to what a nation like Brazil is like, but one could only ever in reality interact with a very limited subset of that whole organization. Thus it is the model that actually enables us to in some way grasp the whole system, but only ever through the use of abstraction that creates a simplified representation of the actual real-world phenomena. A central part of modeling is the use of encapsulation. Encapsulation means to cover or surround something in order to show or express only the main idea or quality of it in a concise fashion. Encapsulation is a central part of modeling and designing systems in that it enables abstraction. Through encapsulation, the internal workings of any component part of a system can be concealed in order for it to reveal only the most essential properties and functionality required for its interaction with other components. In such a way, encapsulation abstracts away the internal details and complexity of a subsystem to enable the effective designing, functioning or vision of the whole system. Abstraction involves the induction of ideas or the synthesis of particular facts into one general theory about something. It is the opposite of reification, which means to make something real, bringing something into being or the making of something concrete. Whereas abstraction is designed to remove the specific, reification involves the opposite process of specification, which is the analysis or breaking down of a general idea or abstraction into concrete specific facts. For any abstraction to have real world application, it must go through a process of reification and specification in which the detail of the abstraction is specified in order to create a real instance of that generic form. All of its real instances must have specific attributes. This process may also be called instantiation. The process of instantiation is one of taking an abstraction, giving it detail form within some real context. This process involves specification, whereby the generic features of the abstract model must be given specific attributes in order to become a specific instance of that generic form and exist within some real world context. For example, if you wanted to build a house, an abstract model designed to describe what a house in the abstract is would not work. You would need one that specified all of the details to your particular house and your particular house would then be an instance of the generic class of all houses. The process of modeling through abstraction invariably requires that one makes certain assumptions and often approximations. For example, in their models Newton assumed that mass is a universal constant whereas Einstein considered mass as being a variable. A model only ever represents some subset of all possible phenomena and thus has to make certain assumptions about other elements and systems outside of our focus of interest. Effective models make explicit the assumptions that they entail, the conditions under which those assumptions will hold and the conditions under which they will not hold and then will be prepared to relinquish the validity of the model under those applications outside of the scope entailed by its assumptions. For example, many of the models that management use in order to deliver their strategy or take a product to market are only applicable under relatively normal market conditions. Management strategy is typically only expected to account for events that are less than a few standard deviations from the norm. However, extreme events do happen and in such circumstances the management team would have to relinquish their model and recalibrate their strategy. Being aware of the assumptions or axioms that support a model is greatly advantageous as it enables the user to know when to apply it and when not and it equally offers the possibility of switching to other frameworks when required and also the opportunity to stay working on those assumptions so as to improve them. The effectiveness of a model may be defined along a number of different parameters. For example, how solid are its foundations? That is to say the assumptions that it's based upon. Are they truly self-evident assumptions or are they contingent upon certain conditions that may not always hold? How well does the model allow us to grasp the whole system and identify its core attributes? Does it truly manage to synthesize all of the different perspectives on that system? How faithful is it to the empirical phenomena? Does it stand up to empirical testing? And moreover, can it predict future events? Models are often evaluated first and foremost by their capacity to match empirical data. Any model inconsistent with reproducing observations must be modified or rejected. A model should have the capacity to explain past observations and predict future observations within some context. For example, one perceived limitation in our standard economic and financial models is that they seem incapable of predicting financial crisis even though they work to a certain degree during normal economic conditions. To be effective, a model must capture diverse information, perspective and views on a particular phenomena. For example, if we take some complex entity like a city, there will be multiple perspectives on how to interpret it. Social, technological, economic, demographic, etc. An effective model must be able to integrate all of these diverse perspectives in some way to give us a vision of the whole system and a basic understanding of its primary constituent parts and relations. And thus, an effective model can be seen to be a balancing act between simplicity through abstraction and synthesis on the one hand and on the other hand, breadth of scope in order to include all of the different views and possible instances of that system. The scope that a model covers is an important metric in its evaluation. Models like that of general relativity are highly valued due to their relevance to any physical system from the scale of a molecule all the way up to the level of the universe. To be effective, a model should contain within it and extend to all instances and applications of the system it is trying to represent. Although models, almost by definition, must be compact representations, they must also be inclusive, entailing within their core form the capacity to derive any possible instance or state to the phenomena under their description. Models are powerful tools in that they enable us to conceptualize large systems that are beyond our immediate faculties and they always do this by removing certain details. If the model is not built properly, that is to say, all relevant information is not represented in the model in a compact form, thus representing only a partial account as to how the world is. If we then use those models to operate in the world, the results may be, at best, only partially successful and at worst, a potentially dangerous situation. In fact, we now have a term for these poorly built mathematical models. They're called weapons of math destruction. Abstract models constrain our perception. They can texturize, frame and condition what we see and don't see. Models that are poorly built and do not include all relevant perspectives and information can blind us to what would otherwise be obvious information. In a very literal manner, models can remove one from one's senses in that they are conceptually based. If they are not adequately built, they can create nonsensical results. One good example of this would be models used in economics that try to describe human actors as rational agents. These models of the rational agent are constructed in a particular way so as to make human economic activity amenable to standard mathematical tools. In the everyday world, it is quite apparent that people do not always consider all actions in a rational fashion. We take many shortcuts, have personal bias, copy others, use heuristics, etc., none of which are rational. But none of this can be properly captured using our standard formal mathematical models and thus it is excluded from standard economic theory. Of course, the world does not change simply because it does not fit into our modeling framework. Whenever there is a mismatch between a model and the empirical world, it is ultimately the model that has to give and break down at some stage. We may go on using limited models because that's all we have, but there will clearly be consequences for doing this. Thus, it is always desirable in modeling that we be explicit about the limitations of a model and work towards developing more robust models whose foundations are more solid, scope broader or match more accurately empirical data and only ever partially trust models because they are always only partial representations of the world.