From the foundation proposal:
“The Maker Governance Framework will be built on rigorously vetted, reproducible, scientific models created by experts with proven track records in the traditional finance space.”
Traditional finance, traditionally, does not take into account fuller understandings around human behaviour as a frontier of financial analysis. Figuring out how people react to stuff seems like an essential component of risk. Does risk not stem fundamentally from individuals behaviour? Yeah, external events and mechanical-technical inputs influence the picture, but humans still are an essential link in the execution of actions within the global economy (and crypto eco). Believing that we are dealing with completely rational-natural selection bound actors neglects much of research around behaviour, modeling and evolutionary biology.
I see this issue as weak point around the current framework of risk assessment for collateral types. We are dealing with people. Ignoring the sociological-psychological-physiological context of humans neglects a more foundational view of risk behaviour. These human variables are inherent to both cdp users, individuals within the internal structure of collateral types and governance/regulatory actors. To attempt holistic scientific risk assessment we cannot simply pick and choose the disciplines we are comfortable conducting analysis in. Here I’m trying to put forward some of the research ive come across relating to the overall context around decision making in humans. I mainly am thinking about risk assessment implications, but this type of research certainly applies to decentralized governance.
To start, here is one reality from stress research that I doubt anyone will argue with.
“Some studies have shown that stress has many effects on the human nervous system and can cause structural changes in different parts of the brain (Lupien et al., 2009[65]). Chronic stress can lead to atrophy of the brain mass and decrease its weight (Sarahian et al., 2014[100]). These structural changes bring about differences in the response to stress, cognition and memory (Lupien et al., 2009[65]). Of course, the amount and intensity of the changes are different according to the stress level and the duration of stress (Lupien et al., 2009[65]). However, it is now obvious that stress can cause structural changes in the brain with long-term effects on the nervous system (Reznikov et al., 2007[89]). Thus, it is highly essential to investigate the effects of stress on different aspects of the nervous system (Table 1(Tab. 1); References in Table 1: Lupien et al., 2001[63]; Woolley et al., 1990[122]; Sapolsky et al., 1990[99]; Gould et al., 1998[35]; Bremner, 1999[10]; Seeman et al., 1997[108]; Luine et al., 1994[62]; Li et al., 2008[60]; Scholey et al., 2014[101]; Borcel et al., 2008[9]; Lupien et al., 2002[66]).”
(Yaribeygi, Habib et al.)
Its well known colloquially that repeated stress impacts long term health. Plenty of research examines the physiological responses and the impacts of stress on our decision making. Stressors include environmental and developmental factors which affect people's sensory and integration responses. Everywhere inflicts stress and is a unique environment, assessing regional contexts that play into people's stress levels could be a useful tool when analyzing risk behaviour of a given actor known to reside or develop in a location. We need methods for gathering data on these components. Surveys and other empirical data aggregated over time will help in deciphering the relationships of actors in the makerdao protocol. That type of works seems to be gaining momentum recently (see https://blog.makerdao.com/understanding-the-dai-user-insights-into-adoption/)Factoring in historical socio-political interactions also helps shed light on the individual context. Increasing complexity becomes apparent quickly. Here, Sapolsky lays out some of the complexities behind analyzing humans (or anything really):
Little will be understood about humans if scientists proclaim the identification of the brain region, or the gene, or the hormone or neurotransmitter that supposedly explains everything. Instead, in order to understand why a human social behavior has occurred, one must factor in neurobiological events 1 s before, but also endocrine events from days before, neuroplasticity from weeks before, epigenetic events in childhood, fetal environment, the genome in a fertilized egg, culture, ecology, and evolution.
(Sapolsky, Robert M. “Doubled-Edged Swords in the Biology of Conflict.)
Thats alotta stuff to consider. Useful in the long term as a goal for risk assessment. Assessing risk on the global scale will require profound understandings around the immense inherent complexities of reality. Global decentralized credit rating agency sounds pretty rad, yet likely unachievable using purely historical scientific patterns. Reductionism serves a purpose, yet neglects fundamental observable interaction between all things (matter or non matter). In other words, we need a stronger sense of context, which requires a merger of academic thought. Some starts on the context of decision making:
“Rather than existing in a vacuum, decisions occur within a context of past experiences and current conditions. While standard models of economic choice assume that decisions are made independent of such factors, empirical evidence indicates that choice can be strongly context-dependent.” (https://www.sciencedirect.com/science/article/pii/B9780124160088000243)
“For instance, Schlösser et al. (2013), offered strong empirical support for the claim that immediate emotions predict risky decisions, beyond the effects of anticipated emotions or the subjective probability attached to outcomes. In a similar vein, Grable and Roszkowski (2008) showed that decision-makers in a happy mood have higher levels of financial risk tolerance, holding bio-psychosocial and environmental factors constant. Using a mood induction procedure, Stanton et al. (2014) reported that a happy mood induction increased risk-seeking behavior compared to neutral mood, whereas a sad mood induction procedure did not induce behavioral differences in comparison to neutral mood.”
(Kusev, Petko et al)
While the textbook and study quoted abovegive plenty to get started with; the science of emotion generation in the brain is hotly contested and undergoing fundamental changes. Here Lisa Feldman Barret puts up part of the case against the incumbent stimulus __> response model (read her book How Emotions are Made for a thorough rundown) and supports a simulation/prediction hypothesis. It may not be apparent, but these types of theories are huge shifts in neurobiological thought and has profound consequences for analyzing human behaviour. Just as we conduct continuous risk analysis remaining agile within the ever changing scientific landscape may prove useful and deserve more resources.
An increasingly popular hypothesis is that the brain’s simulations function as Bayesian filters for incoming sensory input, driving action and constructing perception and other psychological phenomena, including emotion. Simulations are thought to function as prediction signals (also known as ‘top-down’ or ‘feedback’ signals, and more recently as ‘forward’ models) that continuously anticipate events in the sensory environment.10 This hypothesis is variously called predictive coding, active inference, or belief propagation (e.g. Rao and Ballard, 1999; Friston, 2010; Seth et al., 2012; Clark, 2013a,b; Hohwy, 2013; Seth, 2013; Barrett and Simmons, 2015; Chanes and Barrett, 2016; Deneve and Jardri, 2016).11 Without an internal model, the brain cannot transform flashes of light into sights, chemicals into smells and variable air pressure into music. You’d be experientially blind (Barrett, 2017). Thus, simulations are a vital ingredient to guide action and construct perceptions in the present.12 They are embodied, whole brain representations that anticipate (i) upcoming sensory events both inside the body and out as well as (ii) the best action to deal with the impending sensory events. Their consequence for allostasis is made available in consciousness as affect (Barrett, 2017).
I hypothesize that, using past experience as a guide, the brain prepares multiple competing simulations that answer the question, ‘what is this new sensory input most similar to?’ (see Bar, 2009a,b). Similarity is computed with reference to the current sensory array and the associated energy costs and potential rewards for the body. That is, simulation is a partially completed pattern that can classify (categorize) sensory signals to guide action in the service of allostasis. Each simulation has an associated action plan. Using Bayesian logic (Deneve, 2008; Bastos et al., 2012), a brain uses pattern completion to decide among simulations and implement one of them (Gallivan et al., 2016), based on predicted maintenance of physiological efficiency across multiple body systems (e.g. need for glucose, oxygen, salt etc.). (*)
From this perspective, unanticipated information from the world (prediction error) functions as feedback for embodied simulations (also known as ‘bottom-up’ or, confusingly, ‘feedforward’ signals). Error signals track the difference between the predicted sensations and those that are incoming from the sensory world (including the body’s internal milieu). Once these errors are minimized, simulations also serve as inferences about the causes of sensory events and plans for how to move the body (or not) to deal with them (Lochmann and Deneve, 2011; Hohwy, 2013). By modulating ongoing motor and visceromotor actions to deal with upcoming sensory events, a brain infers their likely causes.
In predictive coding, as we will see, sensory predictions arise from motor predictions; simulations arise as a function of visceromotor predictions (to control your autonomic nervous system, your neuroendocrine system, and your immune system) and voluntary motor predictions, which together anticipate and prepare for the actions that will be required in a moment from now. These observations reinforce the idea that the stimulus→response model of the mind is incorrect.13 For a given event, perception follows (and is dependent on) action, not the other way around. Therefore, all classical theories of emotion are called into question, even those that explain emotion as iterative stimulus →response sequences
(*) made by me, personally I see this statement falling in line with thinking around natural selection and a tendency towards fitness, that ignores the evolutionary force of sexual selection ie mate choice. (Hosken, DAvid J. “Sexual Selection.” Current Biology, Cell Press, 24 Jan. 2011, www.sciencedirect.com/science/article/pii/S0960982210015198.) is a start. Richard Prum’s The Evolution of Beauty is a great book on the subject including historical failures to integrate it into analysis.
(Barrett, Lisa Feldman. “The theory of constructed emotion: an active inference account of interoception and categorization.”)
The theory of constructed emotion is outlined in the paper quoted above.Things get interesting when considering the subtleties around risk behaviour in light of these alternative models around emotion propagation and basic neuronal architecture. Understanding the predictive simulations around neuronal architecture may help reveal some of the individual biases on many levels within the protocol.
The materials quoted above are a start for risk or governance folks, if there's interest I can explore the subject further. I'm not formally trained nor ready at the moment to lay out a review and analysis about the specific social-physiological-evolutionary subtleties that affect risk behaviour, but I do believe that if maker wants to stick around innovation around the way people integrate scientific fields is supremely important. While the foundation proposal specifies financial science, nothing exists in a vacuum, especially not finance. Integrative studies by definition are more complex, yet I don't see that as an excuse for ignoring large areas of poignant academic research.
I hold mkr
Yaribeygi, Habib et al. “The impact of stress on body function: A review.” EXCLI journal vol. 16 1057-1072. 21 Jul. 2017, doi:10.17179/excli2017-480
Sapolsky, Robert M. “Doubled-Edged Swords in the Biology of Conflict.” Frontiers in psychology vol. 9 2625. 20 Dec. 2018, doi:10.3389/fpsyg.2018.02625
Kenway Louie, Benedetto De Martino, Chapter 24 - The Neurobiology of Context-Dependent Valuation and Choice, Editor(s): Paul W. Glimcher, Ernst Fehr, Neuroeconomics (Second Edition), Academic Press, 2014, Pages 455-476, ISBN 9780124160088, https://doi.org/10.1016/B978-0-12-416008-8.00024-3.
Kusev, Petko et al. “Understanding Risky Behavior: The Influence of Cognitive, Emotional and Hormonal Factors on Decision-Making under Risk.” Frontiers in psychology vol. 8 102. 1 Feb. 2017, doi:10.3389/fpsyg.2017.00102
Barrett, Lisa Feldman. “The theory of constructed emotion: an active inference account of interoception and categorization.” Social cognitive and affective neuroscience vol. 12,1 (2017): 1-23. doi:10.1093/scan/nsw154
Gilovich T., Griffin D., Kahneman D. (2002). Heuristics and Biases: The Psychology of Intuitive Judgement. Cambridge: Cambridge University Press. 10.1017/CBO9780511808098 [CrossRef] [Google Scholar]
