Image Credit: James Robert White
Executive Summary
The idea of a ‘sustainable complexity floor’ and a ‘sustainable complexity ceiling’ is a type of sustainability expressed in terms of the minimum and maximum complexity level of any given agent, as required for its own longer-term viability. Individual firms, natural organisms, and systems thereof, are subject to complexity floors and complexity ceilings to grow and thrive sustainably, yet without additional measures, they don’t always respect these boundaries[1]. This suggests policy measures can be adopted to both incentivise and, where necessary, regulate, economic agents to respect complexity boundaries for the long-term viability of the economy, as a whole. This can be in the interests of economic stability or in response to pervasive challenges like climate change, where sustainable complexity relates to respecting planetary boundaries. This blog post focuses on firstly explaining the core concept and secondly, how policymaking for sustainable complexity can promote economic stability.
Sustainable complexity in Nature
At one extreme, complex organisms, e.g. those with 50 times the number of human genes, are very rare in Nature. This is because organisms which don’t respect a sustainable complexity ceiling may survive in the short-term, but in the long term, likely won’t[2]. For an example of the converse, that of evolution transgressing a sustainable complexity floor, consider an experiment in the evolution of yeast published in Genetics in 2004[3]:
The experiment showed that yeast cells grown in a lab culture will tend to adapt below their sustainable complexity floor as they mutate. Adaptations which switch off some protein production enable the yeast to adapt to the temporarily simple lab conditions in which certain proteins are simply unneeded. Temporarily, the adapted yeast will do better than rivals in the lab, as the savings in energy can be used for faster reproduction in this lab context. But in the long-term, this then leaves them singularly unviable in the wild, where those lost proteins are, in fact, essential to continued survival.
Sustainable complexity applies to economic agents, too
This cycle of adaptive systems freely adapting to their environment’s temporary simplicity or complexity to take them below or above their sustainable complexity level shows that basic evolutionary forces (acting alone) don't respect sustainability. The same is true of firms and economic agents generally, where sustainable complexity ceilings and floors also exist but are not always respected due to pressure form short-term economic competitive forces or, conversely from internal pressures to grow in complexity (e.g. Parkinson’s law, government intervention). As an example of the former, many economic agents simply ‘make hay while the sun shines’ leading commentators to bemoan the short-termism of the free market. An example of the latter, is the case of the tendency for regulation to grow as time goes on, potentially creating a high cumulative burden[4], [5].
Macroeconomic policy can unwittingly incentivise overly simple strategies
What is less clear is the likely role of macroeconomic policy-making in creating environments which actually disincentivise economic agents from adhering to a sustainable complexity level. A significant factor, albeit perhaps counterintuitive, concerns when policy-makers maintain an artificially simple climate of high economic certainty. Despite business leaders often demanding more certainty from policymakers, this can actually contribute to the lack of robustness of companies over the longer term. Like the aforementioned lab for the yeast cells, an artificial high certainty economic climate means agents see less incentive for minimally complex strategies to be adopted – at least while life is made so beautifully simple by macroeconomic policymaking. If this artificial simplicity is engineered by policies guaranteeing certain economic conditions will be controlled and maintained – until they can’t – such a policy can take a substantial fraction of agents below their sustainable complexity floor, only to then come ‘unstuck’ as the economy veers out of policymaker’s control. An example of such conditions likely contributed to the 2008 crash.
Unsustainable complexity in relation to the 2008 crash
An unsustainable complexity interpretation of the 2008 crash would make the argument that the temporary simplicity of the investment environment in the US housing sector and the ease with which profit was made by overly simple strategies is, for the most part, what led to the maladaptation of strategies by economic agents and the crash that followed. The excessive simplicity of the prior epoch was partly enabled by policymakers who enabled firms to make money selling mortgages on commission regardless of the quality of the loanee’s finances. This took too many economic agents involved in the downstream market below a sustainable complexity floor of behaviour, thus leaving them singularly ill-equipped for the more complex future.
CDO Complexity
This future was, of course, also fast being created by the converse case of the growth in unsustainable complexity of CDOs - those same bad mortgages, packaged into complex financial instruments, which also hid their bad news, via excessive complexity, from simpler economic agents and also via the ratings agency’s summary ratings. Therefore, in terms of CDOs, a maximum sustainable complexity ceiling had also been breached. This prevented both overly-simple agents from tracking the changing complexity level of the financial environment, and so failing to adapt, while the CDOs actually became too complex to be sustainably managed by, even competent, downstream agents.
In this way, we can see the concept of sustainable complexity transgressed at both extremes by different agents, as it panned out in the evolution of a global economic crisis. The subsequent contagion, was then a subsidiary, technically, a secondary compounding factor; but also likely related, in this case, to the growth of unsustainably high complexity of trading arrangements between banks.
Macroeconomic policy should incentivise adherence to sustainable complexity floors and ceilings
In general, the prescription for this type of risk, that of transgressing complexity boundaries, goes beyond adding the certainty of moral hazard, and instead argues for policymakers to take a variety of actions to monitor and regulate the complexity levels of economic agents at each extreme. Policymakers could:
1. Measure and monitor the complexity of economic agents. And invest in simulations to help estimate the complexity floor and complexity ceilings for different kinds of economic agents in various sectors of the economy, formulating policy based on this information.
2. Incentivise adherence of agents to a sustainable complexity floor by the sufficient cycling of macroeconomic conditions using macroeconomic policy, to provide in the present time, the incentive to incorporate sufficient (realistic) uncertainty about future economic conditions. The effect is to incentivise more robust behaviour. Microeconomic policies should also align with this to avoid perverse incentives in specific markets for economic agents to adopt excessively simple strategies.
3. Conversely, policy makers should be aware of the risks of artificially maintained excessive economic certainty, (insufficient uncertainty), in terms of the perverse incentives for unsustainably simple strategies, by economic agents.
4. Control and limit excessively high complexity where it develops in specific markets and where there are excessively large step changes in reporting complexity to agents versus the true underlying complexity, e.g. what was the step change in CDO complexity to the ratings agency summary of it? Further, what was the complexity of trading arrangements between banks? Targeted regulations to monitor and report on transgressing of defined complexity ceilings and step-changes should be introduced, and those that already exist should be kept.
5. Investigate the relationship between growing financial complexity and growing financial regulation as well as the general problem of cumulative regulatory complexity, where it exists[5].
How sufficient cycling of economic contexts and sufficient uncertainty can incentivise robustness
I will now focus on the expected effects of incentivising agents to remain above their own sustainable complexity floor by providing sufficient uncertainty in economic conditions.
A useful analogy with Nature
As a proposed policy lever, the sufficient cycling of economic policy contexts has a direct analogy in Nature to sexual reproduction and the shuffling of genes in each generation of a population, which has long been thought to encourage innovation and avoid sub-optimal gene encoding[6]. Similarly, it also has an analogy in Nature with the role of noise as an adaptive function in microbiological organisms[7].
A policy of allowing the economic climate to change with sufficient frequency, (e.g. as informed by agent-based simulations[1]) would provide an incentive for economic agents to adhere to their long-term sustainable complexity floor. As is the case in Nature, this could benefit the long-term economy by similarly incentivising a range of behaviours that would make the economy, as a whole, more robust. These details for the effects of sufficient uncertainty on economic agents will now be discussed.
The evidence base for the beneficial effect of sufficient uncertainty
Research by Boston Consulting Group found that their “quantitative study of nearly 1,800 companies over 25 years shows that resilience in unfavorable periods accounts for nearly 30% of long-term outperformance.”[8] To help to understand this economic research result likely we need to also consider how in Nature noise, uncertainty and changing conditions in an organism motivates various strategies by cells and genes to adapt to the uncertainty in ways that can have general long-lasting advantages[6]. This shows what effect incentivising a sustainable complexity floor via sufficient uncertainty could have in practice. In an economy, many of the beneficial behaviours incentivised by economic uncertainty are described by Boston Consulting Group. This is my (selective) take on these advantages blended with evidence from Nature:
1. Encouraging a diversity of strategies some agents betting one way, others, another way, as we see in cellular biology in response to noisy signals and processes in the system showing that uncertainty can be adaptive for the system as a whole[7].
2. Diversification of products, systems, markets within each firm is an important response to uncertainty and can lead to long-term robustness shown by Boston Consulting Group[8].
3. Providing incentives for bet hedging by individual agents, and mechanisms for more resilience like war chests or rainy-day investment, also a strategy noted by Boston Consulting Group[9].
4. Encouraging networks between firms to form which is another strategy observed by Boston Consulting Group[9].
5. Incentivising the reducing of costs of switching strategies, which can also be supported by microeconomic incentives, so increasing decision capacity, since successful decision-making is also experiential[10].
6. Firms investing in the reduction of the cost of switching strategies may also have the effect of biasing their strategic solutions to more generally favourable adaptations which are just superior innovations. This is analogous to the effect of shuffling of genes in sexual reproduction (my research, forthcoming).
7. A further likely effect is that uncertainty supports an ecosystem of specialist services that supply resilience and new strategies to firms.
8. Noise in the environment, such as macroeconomic uncertainty, can couple to the agent population so that simpler agents effectively become more complex, passively, without developing or changing their strategy as such; but just from the result of deriving a benefit from their basic response to changing conditions (my research, forthcoming).
9. Boston Consulting Group discuss in more detail a range of effects of firms investing strategies to weather unfavourable economic conditions, most of them beneficial. [8], [9].
References
A Timlett, Video Presentation, www.fractalagility.com 2021 Jan
O Hidalgo, J Pellicer, M Christenhusz, H Schneider… - Trends in Plant Science 2017 July
SB Joseph, DW Hall Genetics. 2004 Dec; 168(4): 1817–1825.
R. Heaton Office of the Parliamentary Counsel 2013 April
Chester S. Spatt Harvard Business Review 2012 June
DJM Crouch Journal of theoretical biology, 2017 Oct
[7] Adaptive Noise
E. Reece S. M Viney. Proc. R. Soc. B 280, 2013 Sept
Boston Consulting Group, 2020 Sept
Boston Consulting Group, 2020 Dec
A Timlett, Video Presentation, www.fractalagility.com 2020 Nov