Quantitative models to measure cyber risk—the same kinds of models widely used by financial services firms—are starting to gain broader acceptance. But risk managers may be led astray if they rely too much on models out of context. What lessons in effectively using models does the financial services industry hold for cyber risk management?
This paper looks at past and presently ongoing examples of innovation to model the timeline of diffusion by building a historical database. We also borrow a basic idea from epidemiology, the basic reproductive number, which we label as the basic diffusion number. We show how to measure the basic diffusion number in a community and[…]
In this paper we explore the effect of the entrance of a disruptive innovation to an existing supply network. The bullwhip effect is often present in forecast-driven supply chains. It refers to a trend of larger and larger swings taking place, and larger amount of inventory needed in response to changes in demand, as one[…]
There exists much uncertainty in decisions of how to allocate resources for innovation. Which new products and services will succeed and which will fail? What is fast growth and what is slow? Two years? Ten years? When evaluating whether to fund or acquire any project (or deciding to discontinue funding) what time period is impatience[…]