Part One: Why Creating Good Innovation Policy is Hard.
For many reasons, innovation strategy is poorly understood and even more poorly executed.
This series of posts will discuss some of my thinking on innovation and creativity, and as such outlines a concept I have developed: the Combinatorial Theory of Innovation from first principles. Others may have written something like this in the past, and if so, super. My concepts draw on methodologies and frameworks in high-dimensional combinatorial spaces that I both created and used at places like NASA and for the DoD for complex systems. For me, these could (and should) be applied everywhere, but especially to how we think about innovation.
I would also like to acknowledge that there are a handful of superb scholars in the field of innovation theory, and I hope there is some overlap in our thinking. There are unfortunately also some less superb innovation scholars roaming the halls of academia and policy and I hope that by outlining my theory of combinatorial innovation, I can more adequately explain why some of their policies are bad, and maybe even at times immoral.
[NB: This set of posts in particular comes from a series of ongoing discussions between myself and Turlough Downes as we create an innovation policy working paper using combinatorial and chaos theory, focusing on Ireland's innovation ecosystem. Thanks Turlough!]
Two Challenges when Creating Innovation Strategy
Today’s policy makers face two core challenges when engaging with innovation strategy.
(1) Firstly, that innovation strategy is a field which is highly complex in nature:
Policy makers are unsure of which direction to drive because the map is very confusing.
(2) Secondly, that innovation strategy has no direct feedback loop to ascertain implemented policy progress:
Policy makers who manage to choose a direction are driving in the dark, unsure if they are navigating in the right direction.
Combined, these two challenges make the field of innovation strategy treacherous to navigate, leaving only the bravest policy makers to try.
Innovationstrategy is complex for many reasons. The dominant cause of complexity is the adjacent nature of innovation strategy to every other policy area. Whilst the basic levers of innovation strategy (to be outlined through this post series) need not be complex, these levers are often considered in the context of other policy areas, which together are complex, and thus complexity is quickly multiplied. For example, innovation strategy has a two way relationship with trade, political economy, taxation, education, fiscal and monetary policy. The complexity of innovation strategy arises when policy makers consider innovation strategy not in light of its own first principles but instead as a second-order effect of other policy domains.
Innovationstrategy’s feedback loop is difficult, nearly invisible to see. The effects of innovation can be analogous to press coverage in news media; they will only appear with either tremendous success or tremendous failure, and are otherwise invisible. Related to the already discussed complex nature, for this reason (invisible feedback) the metrics of innovation strategy are often attached instead to its policy adjacent areas. Thus, innovation strategy and its progress can only be measured as a second- or higher order effect of trade, political economy, taxation and so forth. Often, which of these higher-order effects can be attributed directly to innovation strategy policies are unknown. And herein lies the nexus of the confusion: whilst the result of innovation strategy is often measured as second-order impacts of policy adjacent areas, industrial strategy should not be created through this adjacent areas.
The Rise of Narrative and Discordant Economics in Innovation Strategy
I, alongside a majority of economists and innovation researchers, believe that innovation and entrepreneurship have domestic and global impacts that far outreach purely financial returns. National and global innovation is a core enabler of societal and economic contribution to prosperity. To understand the importance of innovation, it is often easier to imagine the world without it; no electricity, antibiotics or running water. On a micro level, technology offers gains in productivity and efficiencies that lead to competitive practices and as such increase our economic output. At a macro level, innovation can lead to breakthrough technologies that provide huge societal value which can be further exported to create economic and political advantages.
It is therefore not only a necessity, but a moral imperative, to better understand the ecosystem of innovation such that policy makers have better maps and driving conditions to navigate the road to prosperity.
Today, we can see the ongoing dynamics and interaction of both the complexity and immeasurability of innovation strategy is giving rise to two trends:
(1) Firstly is the rise of “narrative economics”.
(2) Secondly is the rise of “discordant economics”.
The rise of narrative economics refers to the rise in economic policies being created not by empirical evidence but instead by the rise in popularity of a narrative throughout society. A recent example is the number of “big government, anti-capitalism” policies that were created in 2020 in response to strong pro-government sentiments that came with the onslaught of the coronavirus pandemic and Black Lives Matter protests.
Within innovation strategy, the rise of narrative economics is a byproduct of its inherent complexity, and the reason for this outcome is twofold.
Firstly, innovation strategy is complex. The policy maker, who often searches but fails to find a unified theory or framework of innovation strategy, must construct a lone theory using piecemeal economics frameworks from adjacent policy areas. By combining micro- and macro-level policies on tax, education, technology and trade reform, the likelihood of creating a policy framework that will explain and drive successful outcomes in innovation is limited.
Secondly, the nature of the feedback loop for the implemented policy makes it difficult, if not impossible, to measure the success of an implemented policy. Thus, the “policy creation to policy feedback” cycle produces a set of wayward incentives for the policy makers’ behavior. The asymmetry between the short-term nature of a policy maker’s tenure within a role and the long-term feedback loop of innovation policy create few to no ways to enable attribution between the policy maker and outcome. In the presence of indiscernible outcomes which lack attribution, the policy maker is inclined to implement a policy that favors objectives in the short-term, whatever they may be. The chances of these objectives being aligned to the long-term objectives of innovation strategy outcomes is low.
This leads to the enactment of “narrative economics”, whereby the economic policy being implemented is driven not by economic theory or proven outcomes but by stories and narratives that motivate and connect policy to political or societal momentum. Narrative economics are prevalent in areas which are poorly understood, too complex to create attribution and feedback loops and have asymmetrical reward profiles between policy maker and policy outcomes.
The increasing prevalence of narrative economics in innovation strategy is concerning for the very reason that innovation strategy needs to be understood: innovation and entrepreneurship are critical to generating and accelerating intergenerational societal and economic gains. Narrative economics emphasizes what is popularly ‘wanted’ within an economy and not what is ‘needed’. In reality, the policy maker exists in a world of tradeoffs, and indeed her primary role is often to allocate resources that are constrained, choosing one policy over another. Narrative economics often fails to deal with complex tradeoffs, which hold unpopular manifestations, and proliferate the idea that even the presence of tradeoffs is nonexistent.
Thus, the path of narrative economics is often the path of least resistance and maximum reward for the policy maker in innovation strategy, who will likely never face the upside or downside of well or poorly chosen tradeoffs without the presence of feedback loops.
Discordant economics refers to a set of economic policies which are implemented simultaneously but which have opposing underlying assumptions or originate within opposing economic belief systems, rendering the set of policies incongruous and as such rendering their impact futile.
An example of this which can be seen in innovation strategy is having both free market capital allocation for innovation simultaneously with government-led capital allocation for the same innovation outcomes. An economy cannot be optimized for free market organization whilst the markets compete with government for ownership within private sector; these policies have conflicting assumptions about the underlying ideology for optimal conditions and cannot coexist.
Discordant economics can often be found where there is a rise in narrative economics. In the face of complexity and indiscernible feedback loops, narrative economics is employed and as such the policies which are adopted do not intend to optimize the stated goals of the system in which they are implemented, but instead achieve the goals of bolstering the defined narrative.
The EU's (lack of) Innovation Policy, (un)Explained
An example of both narrative and discordant economics can be seen in the EU's innovation policy, with the growing narrative that the state should have increased involvement in the micromanagement of innovation: that is, picking winners, allocating funds and taking equity ownership of private investments. The narrative economics, dubbed “mission-oriented policy” of increasing state capacity in the private sectors follows a global sentiment surrounding the rise of increased government intervention and anti-capitalism movements. Some economists have gone as far as to state that certain governments have been crucial to the creation specific private sector corporations, and offers this as unfounded and anecdotal evidence for the need to push the narrative economics of the state’s increased role in picking private sector winners.
This leads to clear discordant economics: as can be see in the EU, there is a simultaneous attempt to grow its private-sector venture capital investment ecosystem whilst participating in the same activities itself through its European Innovation Council Fund (EICF). The EICF is a result of narrative economics, in this example the narrative being that of a “mission-oriented” public sector that is embraced (despite a lacking empirical evidence of its value) in the context of an increasingly anti-capitalist Western narrative, and accelerated by economists such as Mariana Mazzucato (more on this, later).
These two activities- (1) growing the private venture capital ecosystem and (2) creating the EICF, when implemented concurrently, negate each other. The acknowledgement of private capital as being the most efficient way to allocate funds, increase competition and grow innovation is then immediately questioned with the creation of the Innovation Council Fund.
If the private sector is deemed to be more efficient at picking and allocating resources to innovation "winners", then EU funds should be allocated in such a way to promote the private sector leading these activities. However the creation of the government-led venture fund indicates otherwise, in which case the EU should remove its funding for private-sector growth initiatives and double down on a government-led strategy accordingly.
This first post does not seek to argue one side over another (at least yet), but instead point to a critical flaw in policy making that is created by the impossibility of creating innovation strategy due to complexity and feedback loop invisibility. And in this example, that flaw is as follows: that pushing for both private sector growth while concurrently investing in government-as-an-investor just allocates scarce resources to two different, and opposed, ideologies (state vs private sector efficiency) and as such is suboptimal, regardless of which ideology is believed to be true.
Using the example of the EU’s European Innovation Council Fund, we can see one example (of many examples globally) where both:
(1) the complexity of, and
(2) lack of feedback loop (attribution) for innovation strategy
can lead to sub-optimal resource allocation through the rise of:
If we believe that innovation has, and continues to, alleviate poverty, increase prosperity and have overwhelming positive societal outcomes, there is a moral imperative to increase the innovation produced within a jurisdiction (at the regional, national or global level).
However, creating a sound innovation strategy and subsequent policy is really hard.
To accomplish this, we need to move beyond understanding and measuring innovation through second- and higher-order effects of adjacent policy areas and understand the input-output mechanisms of the innovation ecosystem itself. Therefore to eliminate these difficulties and the traps of narrative and discordant economics to create sound innovation strategies, it is vital to understand innovation strategy from first principles.
In the next post, I will start to unpack the definitions of "innovation" and dissect and model the interaction of the components of the ecosystem, which is complex in nature. I will introduce the concept of combinatorial frameworks for innovation and outline what its underlying assumptions can tell us about the nature of innovation growth.