Ethics 2.0

I want to assert that maximization of Ethics is both a conceivable and achievable objective.  To get there I'm going to have to take evolutionary thinking out of the world of "just genes" and apply it generically across a much broader field.


1. The strategy of all entities is to predict and control their circumstances so as to achieve a satisfactory condition.  As a baseline, this satisfactory condition is "reproductive success", that is, an increase in the population of the entity over the long term.  While I want to include the totality of traditional evolutionary theory in this concept, I also want to extend it to include a large number of entities and strategies that flow from and link to this bio-genetic foundation.  When appropriate, I'll use four quite different domains as examples as we work through the discussion.
a) Take a species, say "rabbits".  When it changes the balance of its genotype (expressed in a diversity of individuals) through selective pressures in order to be more fit within a given fitness landscape, it is predicting and controlling its circumstances to achieve a satisfactory condition.
b) Take a plant.  When it turns towards the sun so as to maximize its photosynthetic performance, its is predicting and controlling its circumstances to achieve a satisfactory condition.
c) Take a particular animal say a fox.  When it uses its sensory apparatus to identify food or to avoid a predator it is predicting and controlling its circumstances to achieve a satisfactory condition.
d) Take a human being in a culture.  When it uses its cultural machinery to craft a machine that successfully achieves a result, e.g., using Newtonian physics to craft a rocket that goes to the Moon, it is predicting and controlling its circumstances to achieve a satisfactory condition.

2. Decomposing prediction and control, there seem to be four core features: the abilities to identify signal from noise, to extract consistent patterns, to formulate effective heuristics for action and to execute them.  Collectively this is "intelligence".  Intelligence need not be cognitive, it need not even be neural.
a) Species intelligence lies at the level of genetics and epigenetics.  Mechanisms for preventing mutation and ensuring fidelity of transcription.  Mechanisms for storing latent morphologies (old mutations) and activation of those morphologies in response to environmental conditions. The entire movement of evolution, selective pressures and mutation driving change, is about the progressive collection of species-genetic level intelligence.
b) Plant intelligence plays out its genetic portfolio on response to its particular environment. Roots discovering nutrition and water gradients.  Stems discovering light gradients.  Activation of drought or predator chemistries based on environmental   feedback.
c) Animal intelligence seems to encompass plant intelligence but adds more or less ability to code abstract patterns and to generate flexible responses.  Thus, even a pigeon can learn to recognize a visual symbol as a means to satisfying hunger.
d) Human intelligence takes this animal capability and externalizes it into an abstraction: culture.  Thus, entire populations generate intelligence that collects over many individual human experiences: mushrooms can make you sick, amanita can make you sick, amanita contains Muscimol and Muscimol's primary action is at GABA receptor sites as a potent GABA-A agonist, etc.

3.  All entities refine intelligence through positive and negative feedback.  Positive feedback reinforces success, where our intelligence in fact enables prediction and/or control to achieve a satisfactory condition.  Negative feedback indicates failure, where our intelligence does not  in fact enable prediction and/or control to achieve a satisfactory condition.  Over the long-term, entities fail when they lose sensitivity to feedback and, therefore, become unable to refine their intelligence.  
a)  When a species achieves dominance within its niche, it begins to lose negative feedback.  Local competition is no longer strong enough to force innovation and the species will begin to lock into "more and more of the same".  Because it works.  At least until some new species from another niche manages to break into the happy equation.  Particularly if that new species had been locked into a highly competitive environment with lots and lots of negative feedback driving its intelligence forward.  Prediction and control drops to zero and the species is extinct.
b) A plant's roots seek water.  If they find water they thicken then branch and grow.  If they fail to find water they stop growing and flow that energy in other directions.  A vine grows on a trellis it slowly twists and lengthens until it finds a sold purchase and then it twists and branches.  Or perhaps it finds something that is too hot and it burns and flows its energy elsewhere.
c) A mouse runs across a grassy meadow.  It can move fast and finds lots of food.  The grass is high and shields it from predators.  Positive feedback.  The snow falls.  Running on the surface of the snow allows it to run faster than burrowing through it.  But suddenly it stands out against the background and is easy prey.  Negative feedback. Then a mutation happens where it turns white in the winter and it is able to blend in with the snow in the winter and the grass in the summer.  Intelligence increased.
d) A business invents a new technology within a highly competitive field.  It learns the hard way what works and what doesn't work and struggles to the top.  An intelligent competitor it begins to consolidate its gains and starts to shape the market to its advantage.  First it locks in suppliers and distributors.  The competitors slowly die or are bought.  Then it begins to capture the regulators and sees laws and regulations slowly reinforce its position and strengths.  New competitors are few and far between.  For decades it is successful, perhaps becoming the biggest business in the world.  But it isn't learning anything - its intelligence is staying static, perhaps even going retrograde.  It is losing its technology edge - but rather than innovate (and run the risks of innovation) it entrenches - pushing the niche to keep its old technology viable.  Increasingly its internal bureaucracy becomes entrenched - challenging the status-quo becomes a bad thing.  Innovation goes away and eventually innovators go away.  It begins to lose its ability to create new intelligence.

4.  When an entities' ability to acquire intelligence becomes inadequate to the risks posed by its environment, it becomes extinct.  
a) If a fitness landscape changes more abruptly than the genetic portfolio and mutation rate of a species can handle, it will simply be unable to compete and will go extinct.
b) A plant exudes a toxin that keeps predators at bay.  A new predator comes into the environment that is immune to the toxin.  If the plant is unable to adapt, it will go extinct.
c) A white rabbit is able to avoid the local fox.  The fox learns how to find rabbit warrens.  If the rabbits don't learn how to disguise or avoid, they will go extinct.
d) A business has lost its ability to create new intelligence.  The market moves on and new technologies are invented.  Consumer behavior changes.  Suddenly its revenues begin to decline.  At first it can keep profits up by cutting costs.  But this reduces its ability to create new intelligence and makes the problem worse.  Eventually it collapses.

5.  A general arc of intelligence increase is to become more sensitive to (and more predictive of) positive and negative feedback and, therefore, are more capable of acquiring intelligence.
a) At the genetic level, the only negative feedback is reproductive failure and the only positive feedback is reproductive success.  
b) At a plant level, even basic feedback like more or less light or more or less nutrition make all the difference between flourishing and declining.
c) The development of the neural system allows progressively more nuanced intelligence.  The fight or flight response of the reptile brain is an encoding of the deep negative feedback associated with an inability to effectively survive a threat encounter (i.e. death) and the association of various probabilistically related patterns with those threat encounters is a heuristic for action that enables a higher level of predication and control than, say, random blundering through a threat field.  The reward systems of the brain (serotonin, etc) act as pre-emptive simulators for positive and negative feedback.  Thus, we "feel good" when we engage in an activity that has been probabilistically selected for - allowing us to intelligently seek that activity.
d) But we can become aware of the fact that our pleasure system is only relatively effective and granular - and we can break the linkage between pleasure and nutrition, more discretely identifying what a "satisfactory condition" is and more effectively shaping its result. The increasing sophistication of mechanisms for acquiring intelligence has been the dominant theme of our human evolutionary vector.  All of the various layers of our neuro-cognitive system are oriented towards an effort to identify signal from noise, extract consistent patterns and formulate effective heuristics for action.  Our "feeling system" linking our most basic (largely autonomous) nervous system and endocrine system is just this sort of instrument: burning sensation in hand ==> move hand ==> "feel" pain ==> identify source of sensation as "hot stove" ==> encode avoidance of that pattern.  

* Each progressive layer of our neuro-cognitive system increases the spatial, temporal and complexity scope of our intelligence.  All attempts to optimize intelligence (prediction and control) use the mechanisms that they have available and are within the accuracy and consistency that those mechanisms enable.  Thus, the basic "fish" nervous system has a very short memory and is essentially minor-adjustment reactive (if "hot" then "move arm"). The more complex "reptile" system has the ability to fire off systemic "fight or flight" responses that change significant fractions of our internal dynamic and can link "similar" events to these responses (if "tiger" then "run away"; but if "cat" then ignore).  The mammal brain can model other intelligences and construct relatively complex strategies against a relatively large set of remembered patterns and heuristics.  

6. Imagination is the generation of new mechanisms for prediction and control, the identification of new signals, new patterns, new heuristics.  Imagination is the mechanism of formation of intelligence.  Imagination does not guarantee acquisition of intelligence - it simply enables it by producing new possibilities.   Imagination that results in intelligence is Creativity.  Imagination that destroys intelligence is Error.  
a) Mutation is a form of imagination.  Mutation generates new possibilities to predict or control.  A successful mutation is imagination that resulted in intelligence - i.e. enhanced reproductive success.  A successful mutation is creative.  An unsuccessful mutation is error.  
b) A plant is growing and runs into a barrier.  It branches and "tries out" a different direction.  This leads to a dead-end - error.  It branches again - this leads to a new avenue for growth - creativity.
c) A rabbit encounters a fox and flees, it identifies the pattern associating the smell of the fox with the overall sensation of flight (fear, etc.).  It then imagines the linkage: the smell of fox equals danger therefore flight.  If this linkage proves to be effective, it is intelligence: creativity.  If this linkage proves to be ineffective, it destroys intelligence: error.  Our minds are constantly grasping at these signals, patterns and heuristics in the effort to discover more intelligence.
d) The sumerians invent cuneiform.  This innovation allows for unprecedented creativity and error.  

* If intelligence is position, imagination is velocity.

* Imagination is risky.  At a minimum imagination costs energy.  Worse, imagination can lead to error and destroy intelligence.   

* The neo-cortex layers on-top of the other layers of the neuro-cogntive system to dramatically increase our ability to abstract intelligence (thereby rendering its storage much less costly)and to generate intelligence through purely mental actions (thereby rendering its acquisition much less costly), and to build machines (thereby rendering implementation much less costly).  The principle advantages of these innovations are that they allow the benefits of imagination at very little cost.  Thus, rather than evolving a large furry coat which, if in error is hard to correct, we imagine clothes and fabricate them.  Rather than grow four legs, we domesticate horses.  Rather than fight a tiger and lose, we imagine fighting a tiger and realize it is a bad idea, etc.

7.  Ethics is a machine for predicting and controlling creativity.  It is a system for optimizing/maximizing our ability to discriminate signal from noise, extract consistent patterns and formulate effective rules for action.  Ethics includes logic, mathematics, empiricism, simulation, modeling, collective learning and all other techniques that increase the velocity of our acquisition of intelligence.  Science as it is typically practiced is a sub-set of Ethics.  That is ethical which increases our ability to acquire intelligence.  That is unethical which decreases our ability to acquire intelligence.  

* If intelligence is position and imagination is velocity, ethics is acceleration.   
* The implication of Ethics is that we can get better at (predict and control) our ability to generate creativity, which is to say that we can deliberately increase our imagination', the percentage of our imagination that is intelligence, or both.

8.  The primary cause of all unethical activity can be found in a relatively small set of roots:
     - Failure of the imagination.  No imagination means no creativity which is unethical.  Any action that decreases creativity is unethical.
     - Failure of feedback.  If you cannot discriminate between error and creativity, imagination becomes so much noise.  Any action that reduces the efficacy of feedback (particularly negative feedback) is unethical.
     - Failure of scope.  It is possible to find local maxima that are far from global maxima.  In fact there is no reason to believe that there is any global maximum of creativity.  Any action that assumes that current intelligence is optimal is unethical.  

9.  These roots can combine and hybridize into many particular forms that are all closely related:
- Fear (of failure / risk).  An inappropriate prioritization of error-avoidance over creativity-discovery will lead to a closure of imagination.  This might result from a failure of feedback where, for example, positive feedback is not received for creativity or too much negative feedback is received for error.  
- Substitution of means and ends.  Where an sign of feedback is mistaken for feedback.  For example, our neuro-cognitive system ultimately seeks intelligence, not pleasure.  Pleasure is a part of our reward system which exists as a mechanism to attempt to simplify complex or subtle feedback in order to refine our intelligence.  Thus, the taste "sweet" generates the sensation of pleasure because - within the capabilities of the system that interprets those signals - seeking things that are sweet (i.e., simple carbohydrates) is a statistically optimal heuristic for action. However if we mistake "sweetness" for "intelligence" we can find ourselves increasingly failing to generate a satisfactory condition.
- Short term thinking.  Where a local maximum of positive feedback is prioritized over a longer-term maximum of creativity.  
- Delusion.  An extreme form of substitution of means and ends and short-term thinking - where we prioritize the sensation of positive feedback over true knowledge.  In human beings, the substitution of "happiness" or "pleasure" for intelligence or creativity by believing errors as a result of a failure of feedback is common.
- Dogmatism.  Where we prefer the short term comfort of believing that we have perfect knowledge in exchange for the reduction of imagination.    
- Leadership by fear.  Disrupting feedback by simultaneously providing false negative feedback and by removing true negative feedback.  
- Leadership by efficiency.  By prioritizing wealth (the perception of intelligence) over intelligence, we can achieve a temporary state of perceived success.  This is a form of delusion and at best is a failure of scope.

10.  Maximization of ethics means maximization of creativity which means maximization of intelligence which means maximization of ability to predict and control circumstances to achieve satisfactory conditions.   This formulation is consistent and scalable over an indefinitely long term.  Maximization of ethics will also maximize happiness over the long term, although this is and must always be a surplus value.  No other strategy is consistent and scalable over the indefinitely long term.     

* In particular, maximization of happiness is a delusional strategy.  Happiness is a signpost, much in the same way that pleasure is a signpost.  You are supposed to be happy when you have achieved satisfaction.  When it works, that is fine and happiness can be an effective measure of intelligence.  But happiness is not the same thing as actually achieving a satisfactory condition - it is possible to mistake the end for the means (the sign for the signal).  When you do this you will very quickly lose negative feedback (which of course always reduces happiness) and lose your ability to discriminate creativity from error or intelligence from ignorance.   

11. In general there are two meta-strategies: efficiency and innovation.  An efficiency strategy is focused on extracting the maximum of useful energy from a closed-system: maximizing the amount of time before the system reduces to equilibrium and entropy.  An innovation strategy is focused on opening closed systems - finding new sources of useful energy and "climbing up" the entropy ladder.  Both of these strategies play against the shape of the "long tail" event curve.  Efficiency strategies maximize benefit during the "normal" behavior of the curve and are brittle to the "black swan" events - that can lead to complete extinction.  Innovation strategies play precisely on the low-probability, large consequence breakthroughs.  A pure efficiency strategy is guaranteed to fail over the long term because a closed system will always reach equilibrium.  It is only by innovating and opening the system that long term success can be achieved.  
a) A species that occupies a given fitness landscape for a long enough time will tend to lose its flexibility and become highly efficient (the less efficient forms can't successfully compete). In so doing it becomes brittle to increasingly small changes in the fitness landscape and becomes increasingly vulnerable to catastrophic failure (extinction).    The longer a fitness landscape is static and the "steeper" its fitness slope, the more risk it generates - in the form of forcing efficiency and losing innovation.  
b) Taking it from the micro perspective, we might be tempted to contrast the hot house flower and the weed.  But, in fact, both strategies might be examples of efficiency and both might be examples of innovation.  The weed might be highly efficient at rapid growth in multiple different environments.  The hot house flower might be an example of very tight fit with each given environment.  The real example is punctuated equilibrium - what appears to be a tendency of entities to dramatically increase innovation during a time of crisis.  Thus it is less a question of specific strategy within a fitness landscape than how your strategy tends over time: is your strategy "pro" or "anti" innovation?  Does your strategy over time begin to slide downhill into an increasingly efficient mode (getting better and better at doing the same thing) or does it promote innovation?  In plants this seems to happen more at the level of genetic evolution, but in animals . . .
c) The question of efficiency and innovation becomes much more interesting at the animal level due to the innovation of the relatively complex nervous system and its powers of signal identification, pattern recognition and heuristic formation.  A rabbit that over-identifies a threat situation will waste its energy constantly running away from non-threats. But one that under-generalizes will find itself dinner for a threat that it failed to recognize.  It seems that there is a constant tendency for the vector of development to go towards efficiency over innovation - this is due to the short term benefits of efficiency and the short term risks (costs) of innovation.  If you compare neural architecture, the "mammal brain" must have been a risky innovation back in the day.  Costly to develop and maintain and not particularly useful until other pieces of the puzzle filled-in.  Memory, complex stimulus-response, cognition, these things hang-together.  If you keep them in-play until the whole system is put together you get a take off innovation that pays off big time.  If you pass and allow one of the pieces to specialize you lose out on the entire capability.  A modern lizard might be able to become a really great lizard, but its a long way back to the cynodonts if it wants to start climbing the path that leads to hominid.
d) The cultural layer is rife with these competing strategies.  For now lets look at the traditional economic domain.  Do you climb into the law of economies of scale and get locked into fixed highly efficient processes and products?  Or do you run along as an innovator, getting clobbered in well-understood markets by your more efficient competitors but jumping out on the cutting edge?  It seems that in the story of business, every great business starts innovative and is able to continue its arc of greatness so long as it is able to maintain its innovation and flexibility - even as it scales and becomes more efficient.  But once the tipping-point is crossed and efficiency overcomes innovation two things happen: it expands rapidly (bloating on past victories) and is fundamentally dead in the water (once those past innovations are fully mined it has nowhere new to go).

12.  Science has increased our ability to predict and control our external environment, but for a number of reasons it has failed to apply its methodologies consistently and rigorously to our complete condition (including ourselves).  Thus, economics reduces its scope only to maximization of wealth, rather than maximization of ethics.  Psychology strives to make us happy rather than to make us creative or ethical.  Economics 3.0, indeed perhaps Science 3.0, requires closing this loop.