WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference

Implementing natural capital credit risk assessment in agricultural lending

Agriculture has critical impacts and dependencies on natural capital, and agriculturallenders are therefore exposed to natural capital credit risk through their loans tofarmers. Currently, however, lenders lack any detailed guidance for assessing naturalcapital credit risk in agriculture and are challenged by the fact that the relevant material risks vary considerably by agricultural sector and geography. This paper developsa natural capital credit risk assessment framework based on a bottom‐up review ofthe material risks associated with natural capital impacts and dependencies forAustralian beef production. It demonstrates that implementing natural capital creditrisk assessment is feasible in agricultural lending, using a combination of quantitativeand qualitative inputs. Implementation challenges include the complexity and interconnectedness of natural capital processes, data availability and cost, spatial data analytical capacity, and the need for transformational change, both within lendingorganisations and across the banking sector

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd