Enabling Mission Impact - Funding Strategies for High-Risk High-Reward Innovation

  • Introduce a model for thinking about whitespace that is based on two factors:
    1. The scope of the problem space.
    2. The density of potential solutions providers
  • The success of DARPA’s model of high-risk, high-reward research investments rests on the ability to *select the most valuable research avenues from a rich ecosystem of options*. Thus, a dense innovation ecosystem of potential solution providers is in fact DARPA’s boundary condition – its condition *sine qua non*.
    • They frame the DARPA model in a more narrow view where they are limited to working with existing providers on specific goals
  • Claim that PMs run ~3 Year programs
    • This seems short to me
  • “Bundle of complementary practices” from Funding Breakthrough Research
    • This is a good term for things far beyond DARPA — you can’t just cherry pick the thing you want
  • Most of the piece is focused on the case study of a minaturized GPS module at DARPA vs. The Graphene Flagship project in the EU
    • The notable thing that the authors don’t address is that this work was done in the STO under Anthony Tether. This is both an extremely goal-based directorate and an extremely goal-based director.


This graphic is a good intuition pump for §Program Design


  • PMs in a larger solution space need to be familiar with a far wider range of research, in order to adequately assess the progress of the varied possible solutions developed. Furthermore, with the problem space expanded to include commercialization as opposed to end use in a specific operational setting, PMs seeking to fill their chosen “technological white space” would require not just technical expertise but expertise in the many possible routes to commercialization as well. Consequently, a larger problem space implies that any single PM is much less likely to have all the necessary expertise to assess and curate the increasingly heterogeneous and sparse solutions that fall within their program’s problem space.
  • For example, the biopharmaceutical and renewable energy industries can be thought of as having similarly sized problem spaces – with one seeking to cure all manner of ills, and the other to develop novel sources of energy.
    • This seems wrong to me — my hunch is that the size of a problem space has a lot to do with the dimensionality of the output
  • Specifically, recognizing graphene’s value for optoelectronics, DARPA’s PM focused their activities on two existing programs. First, under DARPA’s Wafer Scale Infrared Detectors (WIRED) program, which sought to “[develop] a high-performance, low-cost detector technology using wafer-scale fabrication techniques” they invested in projects such as a graphene-enhanced infrared detector with greater light absorption and tunability, to which it provided $1.3M USD of funding . In other words, DARPA applied its model within the denser ecosystem of wafer-scale technology, investing in graphene as only one of the many projects in the program’s portfolio of possible solutions. Hence DARPA operated within the boundary conditions where its research investment model would be most successful. In parallel, other graphene investments undertaken by DARPA were focused narrowly on the development of an implantable graphene-based electrode capable of measuring both optical and electrical brain
    • This is interesting because it suggests that it is important to go at whitespace sideways through existing applications that could be improved. Something something Adjacent Possible — ::this seems important.::
  • Its success relies on the ability to prioritize possible research avenues across an ecosystem, select the most valuable ones to pursue further, and down select others to be stopped (From Funding Breakthrough Research again)
  • In this case, a program seeking to make high-risk, high-reward investments must first seed the ecosystem with potential solution providersbefore having any to select from and invest in.

  • Different tactics for different quadrant transitions
  • Entry can thus be incentivized through a ‘competition’ model
  • The strategy for Mission Arena 4 therefore lies in *concentrating*the efforts of its disparate solution providers
  • One example of this in action is XPrize’s “Impact Roadmaps”, where a very broad goal, such as securing the future of food or the future of housing, is analyzed by a panel of existing solution providers, and subdivided into potential sub- goals. These sub-goals then serve as the *temporary*right-side objective to guide their competitions which, as introduced before, serve to attract solution providers. As more solution providers enter the space, and gain more experience with the challenges and opportunities of a given problem space, the current roadmap is then reassessed to define a more accurate right-side objective over time.
  • You could think of different “moves” you can make
    • Widening
    • Narrowing

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