A Lodestar for Complex Systems


Joint Program in Real and Financial Markets


Kenwave Research plus MintKit Institute

Following its debut in 2011, the MintKit Institute has pursued a wide-ranging program of research on vital issues in finance and economics. The studies have unveiled the true nature of the marketplace and refined basic strategies for ample growth at modest risk. The topics in focus range from beguiling myths and rampant mistakes in the financial forum to secular trends and public policies in the real economy.

In this endeavor, MintKit is now joined by a kindred spirit in the form of Kenwave Research. The mission of the latter is to develop supple tools for decision making in complex fields. For this purpose, the core techniques span the rainbow from multivariate models and robust statistics to neural networks and genetic algorithms. Moreover, an integrated approach that combines two or more methods can yield synergetic results that enhance the strengths of elemental schemes while bypassing their respective flaws.

Hybrid methods of this sort can excel in knotty domains rife with chaotic structures and erratic events. The applications of the methodology range from scientific discovery, medical diagnosis, and business strategy to financial forecasting, socioeconomic planning, and public policy.

The programs at MintKit and Kenwave display some similarities as well as differences. An example of a shared trait lies in the systematic approach to probing cryptic systems and making deft decisions at the frontiers of innovation and enterprise.

A related hallmark resides in the dual strategy of plumbing the innate nature of murky domains while forging trenchant solutions to tricky problems. In particular, a descriptive portrait of a mazy system captures the pith of the subject despite the mantle of myths and misconceptions that confounds the world at large. Meanwhile, a prescriptive template corrals the findings and provides the groundwork for wholesome action.

From the converse stance, MintKit and Kenwave differ in crucial ways. A key distinction concerns the objectives of the research agenda. To wit, MinKit pursues a spectrum of applications geared toward bracing growth in a global marketplace along with salient functions such as financial forecasting and public policy. By contrast, Kenwave assumes a technical slant keyed to pliant tools for complex tasks regardless of the domain.

Despite their separate charters, however, the two parties share a common interest in the area of decision making in financial economics; that is, the crossroads of dicey markets and stringent methods. The intersection of ambits affords plenty of opportunities for collaboration. The promising projects may be classified into two broad types: factual knowledge to capture the marrow of the marketplace as well as mantic modeling to pave the way for cogent action.

Moreover, the shared interest in finance and economics represents the mainstay of the research program at Kenwave in the early stages. The role of the newcomer centers on quantitative studies to assess the qualitative models developed at MintKit. To this end, Kenwave draws on a medley of extant and newborn techniques in data science in areas ranging from protean graphics and nonparametric statistics to causal modeling and machine learning.

To round up, the partnership between MintKit and Kenwave entails a series of creative projects dealing with the real and financial markets. The case studies make use of ductile tools to fathom abstruse systems abounding in chaos and complexity. The resulting harvest of insights and guidelines provides the fodder for passive frameworks as well as active templates to bolster decision making in diverse domains.

The audience for the joint studies includes the readers of the reports prepared by MintKit Institute as well as the users of the software crafted by Kenwave Research. The interaction of the research hubs renders a bounty of rewards to both parties. The ultimate beneficiary is the global community of stakeholders that partakes of the results.

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