As all forms of governance are tied to the economic system that they are based on, I provide some background on what constitutes 'value', socially, ethically, economically and in the marketplace, as cited in the first linked Wikipedia article [however, the first attached image shows that AI, such as AlphaGo, can use policy networks to develop Value nets (even in the face of uncertainties) separately than that determined by the greed-based market place].
https://en.wikipedia.org/wiki/ValueExtract: "Value (ethics), it may be described as treating actions themselves as abstract objects, putting value to them
- Social imaginary is the set of values, institutions, laws, and symbols common to a particular social group
- Value (economics), a measure of the benefit that may be gained from goods or service
- Theory of value (economics), the study of the concept of economic value
- Value (marketing), the difference between a customer's evaluation of benefits and costs"
Theory of value (economics)
https://en.wikipedia.org/wiki/Theory_of_value_(economics)Marginal utility
https://en.wikipedia.org/wiki/Marginal_utility#Marginalist_theoryExtract: "The marginalists of the revolution, however, had been formally concerned with problems in which there was neither risk nor uncertainty. So too with the indifference curve analysis of Slutsky, Hicks, and Allen.
The expected utility hypothesis of Bernoulli and others was revived by various 20th century thinkers, with early contributions by Ramsey (1926), von Neumann and Morgenstern (1944), and Savage (1954). Although this hypothesis remains controversial, it brings not only utility, but a quantified conception of utility (cardinal utility), back into the mainstream of economic thought.
A major reason why quantified models of utility are influential today is that risk and uncertainty have been recognized as central topics in contemporary economic theory. Quantified utility models simplify the analysis of risky decisions because, under quantified utility, diminishing marginal utility implies risk aversion. "
The following Wikipedia article is entitled "Theory of Games and Economic Behavior".
https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_BehaviorExtract: "Theory of Games and Economic Behavior, published in 1944 by Princeton University Press, is a book by mathematician John von Neumann and economist Oskar Morgenstern which is considered the groundbreaking text that created the interdisciplinary research field of game theory. In the introduction of its 60th anniversary commemorative edition from the Princeton University Press, the book is described as "the classic work upon which modern-day game theory is based.""
I provide the following quote from the linked Wikipedia article entitled: "Prisoner's dilemma" (see also the second image related to the influence of uncertainties on decision making in the prisoner's dilemma), that indicates that in 'wicked problems' like climate change, uncertainty makes it much less likely that we will avoid climate catastrophe than for cases follow clear rules.
https://en.wikipedia.org/wiki/Prisoner%27s_dilemmaExtract: "In environmental studies, the PD is evident in crises such as global climate-change. It is argued all countries will benefit from a stable climate, but any single country is often hesitant to curb CO2 emissions. The immediate benefit to any one country from maintaining current behavior is wrongly perceived to be greater than the purported eventual benefit to that country if all countries' behavior was changed, therefore explaining the impasse concerning climate-change in 2007.
An important difference between climate-change politics and the prisoner's dilemma is uncertainty; the extent and pace at which pollution can change climate is not known. The dilemma faced by government is therefore different from the prisoner's dilemma in that the payoffs of cooperation are unknown. This difference suggests that states will cooperate much less than in a real iterated prisoner's dilemma, so that the probability of avoiding a possible climate catastrophe is much smaller than that suggested by a game-theoretical analysis of the situation using a real iterated prisoner's dilemma.
Osang and Nandy provide a theoretical explanation with proofs for a regulation-driven win-win situation along the lines of Michael Porter's hypothesis, in which government regulation of competing firms is substantial."
Next I note that the following linked article entitled: “AI Can Beat Us at Poker—Now Let’s See If It Can Work with Us”, discusses how AI research involving games like the Prisoner's Dilemma can be used to investigate how to increase human cooperation:
http://www.cbs.com/shows/big_bang_theory/video/QZ3PzCic8hVPupmysSDqIH8fU_nuzkdD/the-big-bang-theory-the-separation-agitation/Extract: “The algorithm that achieved that calculates some promising strategies for the game being played in advance, before learning which to use based on the actions of its co-player. It isn’t likely to become the foundation of future human-robot relations, but is intended to show how experiments can test cooperation, and inspire further research into the idea, says Rahwan.”
Finally, I note that the following reference makes it very clear that most humans (even most experts) have a very weak intuitive understanding of their own ignorance (which results in a poor understanding of gambles/risk; a situation that hopefully T-4IR will work to improve).
Ole Peters and Murray Gell-Mann (Feb. 2, 2016), "Evaluating gambles using dynamics," Chaos, DOI: 10.1063/1.4940236
http://scitation.aip.org/content/aip/journal/chaos/26/2/10.1063/1.4940236Abstract: "Gambles are random variables that model possible changes in wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but their generality limits predictive power. Expectation value maximizers are defined as rational in economics, but expectation values are only meaningful in the presence of ensembles or in systems with ergodic properties, whereas decision-makers have no access to ensembles, and the variables representing wealth in the usual growth models do not have the relevant ergodic properties. Simultaneously addressing the shortcomings of utility and those of expectations, we propose to evaluate gambles by averaging wealth growth over time. No utility function is needed, but a dynamic must be specified to compute time averages. Linear and logarithmic “utility functions” appear as transformations that generate ergodic observables for purely additive and purely multiplicative dynamics, respectively. We highlight inconsistencies throughout the development of decision theory, whose correction clarifies that our perspective is legitimate. These invalidate a commonly cited argument for bounded utility functions."
Also see:
http://www.newswise.com/articles/exploring-gambles-reveals-foundational-difficulty-behind-economic-theory-and-a-solutionExtract: " In the wake of the financial crisis, many started questioning different aspects of the economic formalism.
This included Ole Peters, a Fellow at the London Mathematical Laboratory in the U.K., as well as an external professor at the Santa Fe Institute in New Mexico, and Murray Gell-Mann, a physicist who was awarded the 1969 Nobel Prize in physics for his contributions to the theory of elementary particles by introducing quarks, and is now a Distinguished Fellow at the Santa Fe Institute. They found it particularly curious that a field so central to how we live together as a society seems so unsure about so many of its key questions.
So they asked: Might there be a foundational difficulty underlying our current economic theory? Is there some hidden assumption, possibly hundreds of years old, behind not one but many of the current scientific problems in economic theory? Such a foundational problem could have far-reaching practical consequences because economic theory informs economic policy.
As they report in the journal Chaos, from AIP Publishing, the story that emerged is a fascinating example of scientific history, of how human understanding evolves, gets stuck, gets unstuck, branches, and so on.
…
The key concepts of time and randomness are at the heart of their work. "Questions of an economic nature stood at the beginning of formal thinking about randomness in the 17th century," he explained. "These are all relatively young concepts -- there's nothing in Euclid about probability theory." Think of it simply in terms of: Should I bet money in a game of dice? How much should I pay for an insurance contract? What would be a fair price for a life annuity?
"All of these questions have something to do with randomness, and the way to deal with them in the 17th century was to imagine parallel worlds representing everything that could happen," Gell-Mann said. "To assess the value of some uncertain venture, an average is taken across those parallel worlds."
This concept was only challenged in the mid-19th century when randomness was used formally in a different context -- physics. "Here, the following perspective arose: to assess some uncertain venture, ask yourself how it will affect you in one world only -- namely the one in which you live -- across time," Gell-Mann continued.
"The first perspective -- considering all parallel worlds -- is the one adopted by mainstream economics," explained Gell-Mann. "The second perspective -- what happens in our world across time -- is the one we explore and that hasn't been fully appreciated in economics so far."
The real impact of this second perspective comes from acknowledging the omission of the key concept of time from previous treatments. "We have some 350 years of economic theory involving randomness in one way only -- by considering parallel worlds," said Peters. "What happens when we switch perspectives is astonishing. Many of the open key problems in economic theory have an elegant solution within our framework."
In terms of applications for their work, its key concept can be used "to derive an entire economic formalism," said Peters. In their article, Peters and Gell-Mann explore the evaluation of a gamble. For example, is this gamble better than that gamble? This is the fundamental problem in economics. And from a conceptually different solution there follows a complete new formalism.
They put it to the test after their friend Ken Arrow -- an economist who was the joint winner of the Nobel Memorial Prize in Economic Sciences with John Hicks in 1972 -- suggested applying the technique to insurance contracts. "Does our perspective predict or explain the existence of a large insurance market? It does -- unlike general competitive equilibrium theory, which is the current dominant formalism," Peters said.
And so a different meaning of risk emerges -- taking too much risk is not only psychologically uncomfortable but also leads to real dollar losses. "Good risk management really drives performance over time," Peters added. "This is important in the current rethinking of risk controls and financial market infrastructure."
This concept reaches far beyond this realm and into all major branches of economics. "It turns out that the difference between how individual wealth behaves across parallel worlds and how it behaves over time quantifies how wealth inequality changes," explained Peters. "It also enables refining the notion of efficient markets and solving the equity premium puzzle."
One historically important application is the solution of the 303-year-old St. Petersburg paradox, which involves a gamble played by flipping a coin until it comes up tails and the total number of flips, n, determines the prize, which equals $2 to the nth power. "The expected prize diverges -- it doesn't exist," Peters elaborated. "This gamble, suggested by Nicholas Bernoulli, can be viewed as the first rebellion against the dominance of the expectation value -- that average across parallel worlds -- that was established in the second half of the 17th century."
What's the next step for their work? "We're very keen to develop fully the implications for welfare economics and questions of economic inequality. This is a sensitive subject that needs to be dealt with carefully, including empirical work," noted Peters. "Much is being done behind the scenes -- since this is a conceptually different way of doing things, communication is a challenge, and our work has been difficult to publish in mainstream economics journals."
Their results described in Chaos are easily generalized, which is necessary to reinterpret the full formalism. But it "may not add very much in practical terms, and it gets a little technical." So that's a future "to-do item" for Peters and Gell-Mann.
"Our Chaos paper is a recipe for approaching a wide range of problems," said Peters. "So we're now going through the entire formalism with our collaborators to see where else our perspective is useful.""
