Sunday, November 13, 2016

What is Modular-Finance ?

The notion that we can predict the future with spreadsheets and equations is a complete delusion..!!!

Tuesday, November 8, 2016

Modular Clusters... Design Rules...

By definition, the value created by a new design be will be split in some fashion between designers, producers, investors, and users. The way value is split in turn will depend on:

* the nature of the contracts that bind designers and investors together;

* conditions of entry to the design process;

* the anticipated structure of one or more specific product markets;

* the patterns of competition that ultimately prevail in those markets.

 Complicating matters further, each of these elements may change over time as the design and its value evolve. (p 16 )

Macros, Micros, Markets... Metrics & Math ~ MFiM... Modular-Finance~in~Motion™

Monday, November 7, 2016


While there has historically been variation in the use of the term, in some communities "modularity" has come to refer to the extent to which a mechanism is functionally specialized (Barrett and Kurzban 2006).It was introduced into broad use within cognitive science by Fodor (1983), who concluded that while some systems in the mind (e.g., sensory systems) were modular, according to his use of the term, others were not. This conclusion rested on the idea that the ties, which Fodor associated with modularity depended on how many properties, which Fodor associated with modularity (e.g., automaticity, fast operation), the system in question had.
     In current use, the issue is less about how many of the properties that Fodor associated with modularity a given system possess, and more of a guide for investigation. Present conceptions of modularity focus attention on the empirical question how modular, or functionally specialized, a putative mechanism is. This approach follows the suggestion that modularity was not an all-or-none property, but a property that a computational system can have to a greater or lesser degree (Fodor 1983).
     To take one example from a well-understood model, consider the visual system. The front end of the visual system consists of photoreceptors, which are sensitive to the presence of light, and fire depending on the wavelength of light that hits them. Their function is to detect light and send information about its presence downstream to the visual system, more generally, is specialized as well, designed to use incoming light, as well as knowledge in the rest of the brain, to construct an image of the outside world, which can then be used to identify objects, plan motion, and so forth.
     A potentially important feature of modular systems is that they can be "walled off" from other systems. (Fodor used the term "informationally encapsulated" to refer to this property.) To return to the example of the visual system, consider the Miller-Lyer optical illusion (Figure 8.1), to which many, but not all, people are susceptible (for further details, see Henrich et al. 2010). If one disregards the arrowheads or fins at the end of these lines, are the lines the same length or is one longer?

Sunday, November 6, 2016

MFiM ~ Abstract

This chapter calls for an approach to economic policy that takes evolutionary and complex systems theories into account. Such an approach alters the way that economic policy is framed and how policy co-depends on understanding markets as outcomes of nonmarket interactions, incomplete information, path dependency, and coordination failures. Using several illustrative examples, the chapter explores the application of evolutionary and complexity thinking to policy criteria, goals, instruments, and policy assessment. These examples—the transition to a low carbon economy, using multilevel selection to inform group design in various human organizations, policy making as shaping and creating markets, government failures in Greek farm policy, and protecting the Sudd Wetland in South Sudan—are used to identify key issues for an evolutionary and complexity approach to public policy.


Many complexities in our world come about through the use of preexisting purposeful information. This information may be structured in various ways (e.g., instructions, recipes, algorithms, rules, rules of thumb, business plans, and expert knowledge) and, if followed, directs the formation of something which otherwise would not have existed. This chapter argues that information organized in this way must ultimately arise as the output of an evolutionary computation. Because of this, an evolutionary process underlies most everything that characterizes human existence. This principle includes economics and markets. This chapter addresses whether or not understanding the fundamental role of evolutionary computation for enabling human and biological complexity provides useful insight into market behaviors and introduces the basic concepts necessary to have this discussion.

Saturday, November 5, 2016


If one accepts this ontological position, that the economy is complex, evolutionary, and reflexive, then one can start building a set of theories and models to describe it in those terms, and test those models empirically. In his paper, Romer calls neoclassical macroeconomics “post-reality economics”. He’s right. So the key challenge is whether we can use these new tools and ideas to describe the economy as it really is – a glorious mess of real human behavior, social networks, cultures, institutions, politics, and innovation – rather than the sterile idealized account of neoclassical theory. We know from economic history that the economy is an incredibly dynamic system, from the explosion of growth unleashed by the industrial revolution, to the booms and busts of financial crises, to the co-evolution of technologies and institutions. This doesn’t look much like an equilibrium system, and traditional economics has struggled to explain these phenomena. A key test then is whether a complex, evolutionary, reflexive view can do better.