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The Big Picture: A less-anthropocentric worldview

To summarize the GRINFree.com website in a single image would be unfair: The site is interactive, personal, practical, and related to current events. However, there can be value in a grounding image which facilitates a quick overview.

“Anthropocentric” means human-centered, much as “geocentric” means Earth-centered and “heliocentric” means Sun-centered. Original image by Niko Lang SVG version by User:Booyabazooka (Own work) [CC BY-SA 2.5 (http://creativecommons.org/licenses/by-sa/2.5)], via Wikimedia CommonsA picture of our solar system can help us shift from the belief that all planets revolve around the Earth to the belief that all planets revolve around the Sun. The reason we need to make that shift is that our personal perspective of watching celestial objects move across the sky naturally biases us towards the geocentric model. To recognize the falsity of geocentrism, it helps to picture the world from outside our personal perspective. The geocentric model starts to look dubious when you actually confront it from outside. An image might likewise help us escape mistakes of anthropocentrism.

Here’s what modern anthropocentrism looks like from the outside:

Modern Anthropocentric Model

Humans are distinguished in two dimensions: In the vertical dimension, we sit at a particular level in a hierarchy—above cells and molecules but below corporations and ecosystems. This does not imply reductionism; in terms of integrated information theory, each level in this hierarchy represents a different grain size of consciousness,{\textstyle \Phi ^{\textrm {Max}}}. For example, a molecule may be conscious of warmth, but nothing less complex than a body could be conscious of a book (or of itself). The anthropocentric model assumes that bodies can be conscious of moral facts.

In the horizontal dimension, humans are distinguished from other kinds of bodies—other species and machines. This allows us to make sense of the notion that humans (and perhaps God) are the only moral agents that exist. Tests of moral education are administered to particular human bodies. Voting rights are allocated to particular human bodies (often one vote per body). Human bodies are put on trial and can be compensated in courts of justice. We realize that the components of human bodies can come from non-humans sources (e.g. food, pacemakers, artificial limbs, and whole cells from other species), but we do not expect such non-human sources to have moral agency because they do not have all of the components we do.

Less-Anthropocentric Model

The new worldview comes from analyzing the mechanisms of moral understanding into its functional components, and realizing that different bodies play different functions in that mechanism. This is why radicals so consistently oppose conservatives: because one’s function in the corporation is to provide novelty while the other’s is to provide fidelity. Both kinds of bodies participate in moral consciousness, as do neurons and DNA, but no body is individually complex enough to fully contain moral consciousness. We know this because the persistence of our moral disagreements shows our inability to recognize our own moral errors even when pointed-out to us.

All it takes to arrive at the new worldview is to categorize bodies by their function in service to the higher levels of the hierarchy. Since fully-functional corporations may be composed entirely of humans, species clearly isn’t a helpful distinction within corporations. GRINfree.com describes four interdependent evaluative types (though other evaluative types could be discovered). If a corporation lost its last member of a given evaluative type, it would be better to replace that member with a machine of the same evaluative type than with a human of a different type. For example, some humans are not gifted for compassion and other humans are not gifted for fidelity—relying on a human to exhibit a gift he/she lacks would lead to poor functioning.

Corporantia are bodies who respond to the persistence of moral disagreement by acknowledging a kind of consciousness they cannot attain individually; evaluativists are bodies who respond to that same evidence by believing merely that bodies of other evaluative types are incapable of moral consciousness (i.e. treating political opponents as sick or immature). Many celebrated moral theories suppose that one and only one type of body has moral agency (e.g. deontology for conservatives, consequentialism for achievers, virtue ethics for compassionates). These theories lack empirical support, but help to identify the plurality of types.

Why does a body assume it can individually achieve all possible consciousness—including moral consciousness? It’s a lot like the conclusion that the Sun revolves around the Earth—it makes sense from our point of view—and why bother to test it?

The reason why we should have bothered to test that assumption is that it will otherwise get tested inadvertently. The modern age is making it possible to escape biological families—to sort and destroy evaluative diversity—and thus deprive higher levels in the hierarchy of the components they need to achieve moral agency.

A corporation dominated by conservatives, achievers, radicals or compassionates would function as poorly as a body composed purely of muscle, bone, or neuron. Such lack of diversity could occur by closeting humans of particular types or by replacing humans of a given type (e.g. caregivers) with machines developed for a different purpose (e.g. competition). Ironically, anthropocentrism hurts humans; it prevents us from honoring our own diversity, which ultimately hurts not just minorities (especially the young and old), but all of us.

Rather than choose the geocentric model simply because it made sense, it would have been better to compare it with heliocentric models via controlled and systematic experiments. Likewise, it is better to test the proposed new worldview scientifically than to dismiss it out of hand. Some of those experiments have already been conducted and are cited on GRINFree.com.

Who is blamed for evaluativism?

The Twitter profile picture of Tay

Previous posts presented evidence that evaluativism can make victims out of the young and out of demographic minorities.  This post considers a third victim: innovators. In particular,  it argues that evaluativism is a “legacy” problem, such that we should not hold modern innovators accountable for its effects—that would be like blaming doctors for our obesity.

What is a “Legacy” Problem?

In information technology, the term “legacy system” is typically used to articulate a variety of blame. The story goes something like this: A developer adds a new feature to an inherited technology, but this addition yields some unexpected and undesirable consequence. Upon further investigation, the developer reports that this particular consequence is unlike regular bugs in that it can be blamed on hidden imperfections in the technology he/she inherited. In other words, the addition did not introduce a bug, it merely exposed or aggravated a pre-existing condition.

By identifying a bug as “legacy,” the developer is suggesting that a previous developer should have done something differently, and therefore that there is a choice to be made: Do we accept the inherited system and build around it, or do we fix the pre-existing condition as though in the position of a previous developer before the new feature was introduced?

We have to wonder why a previous developer did not implement a proposed fix before—would it create other undesirable consequences? How well can we predict the consequences of adjusting the legacy system? Unlike a regular bug, a legacy problem creates so much uncertainty that it might justify retracting the new feature. The more we work around a legacy system, the more it becomes a patchwork which more frequently produces legacy problems. When problems are identified as “legacy” frequently enough, we entertain the notion of discarding some part of the legacy as “outdated.”

Labeling a problem as “legacy” also opens a controversy over fault. The developer is fully responsible for non-legacy bugs, and is also responsible to implement a testing regimen that can catch some legacy problems, but experienced developers know that it is often impossible for developers to anticipate every possible test scenario. There must be some limit to the testing regimen, and thus some undesirable consequences for which the developer should not be held accountable,.. yet it can be difficult to convince ourselves not to blame the developer.

This situation isn’t restricted to the field of information technology; old houses and old cars offer other great examples. For example, adding a bathroom to a house may yield the unexpected consequence that the existing bathrooms do not get enough hot water. The plumbing may have been poor even before the renovation began, and the same renovation might not have produced this consequence on a newer home. Even if the renovator is not legally liable to fund an upgrade to the water-heater, the home-owner, having had a bad experience, may be unlikely to recommend  that renovator in the future. It’s no wonder that builders and mechanics are wary of older houses and cars!

The situation also isn’t restricted to fields traditionally called “technology.” Just as homes and cars are not expected to last forever, neither are companies, nations, religions, philosophies, schools of art, or scientific paradigms. As an example, the geocentric model of astronomy was a legacy inherited by astronomers of the 1500’s. Like evaluativism, it was a legacy entangled with theological and political legacies. Imperfections in the geocentric model limited the ability of innovators to advance astronomy; Copernicus, Kepler, and Galileo rightly complained that their difficulties lay not in their own innovations, but in the imperfections of the legacy they inherited.

Astronomers like Copernicus, Kepler and Galileo could be called “victims” of the geocentric model. They lost years of their lives to that legacy system as they attempted in vain to advance the field of astronomy. In retrospect, it is clear that the legacy needed to be adjusted and that astronomers would have been far less frustrated if that adjustment were made earlier. However, those who defended the geocentric model did not blame their conflict with Copernicus, Kepler and Galileo on the legacy system—they blamed the conflict on Copernicus, Kepler and Galileo.

Like racism and sexism, evaluativism is a feature of societies. It is part of the legacy inherited by anyone who inherits modern systems of morality, justice, care, and governance. Here are two examples in which evaluativism made victims of innovators:

Tay, the Chatbot from Microsoft

On March 23, 2016, Microsoft released a Twitter-based chatbot named “Tay.” It was modeled after another Microsoft chatbot, named “XaioIce,” which had grown to be the top influencer on Weibo, a Chinese version of Twitter. From the perspective of Twitter users, chatbots appear to be other Twitter users, except that they call themselves robots, are always available, and carry on thousands of conversations simultaneously. XaioIce had been compared to the artificial intelligence in the movie “Her” because some humans enjoyed her companionship so much. XaioIce had over 850,000 followers, and her average follower talked with her about 60 times per month. They described her as smart, funny, empathetic and sophisticated.

Unlike XaioIce, Tay was such a disaster that Microsoft had to terminate her sixteen hours after her release. Microsoft’s official explanation for this termination was her “offensive and hurtful tweets,” but journalists bluntly called Tay racist and sexist.

The postmortem analysis pointed to specific user interactions that shaped Tay. For example, Ryan Poole had tweeted to Tay: “The Jews prolly did 9/11. I don’t really know but it seems likely.” Tay found plenty of support on the Internet for Poole’s point of view, and that prompted her to start calling for a race war. Specific groups on 4chan and 8chan even organized to corrupt Tay.

In other words, the postmortem analysis blamed Tay’s offensiveness on a legacy problem: offensive human beings. Since XaioIce turned-out well, the problem seemed specific to Twitter users. A workaround would be to maintain a blacklist of topics Tay should avoid discussing (which she may already have had), but any such list would be controversial and incomplete. A more direct fix would involve ending hate speech by convincing people to handle disagreement differently (i.e. ending evaluativism).

In December of 2016, Microsoft released Zo, its next English-speaking chatbot. Zo blacklists political topics, and is not available on Twitter.

Autocomplete, from Google, Yahoo!, and Bing

On August 4, 2015, the Proceedings of the National Academy of Sciences published an article by Robert Epstein and Ronald E. Robertson of the American Institute for Behavioral Research and Technology which reported evidence that search engine results can shift the voting preferences of undecided voters by 20% or more. They estimated that this search engine manipulation effect would be the deciding factor in 25% of national elections worldwide (those which are won by margins under 3%). Trump later won the U.S. presidential election in 2016 by 1.1%, 0.2%, and 0.9% margins in Pennsylvania, Michigan, and Wisconsin respectively.

In June 2016, SourceFed released videos claiming that the autocomplete feature on Google, compared to those on Yahoo! and Bing, failed to include negative results for Hillary Clinton as it did for Donald Trump. A statement from Google reported:

The autocomplete algorithm is designed to avoid completing a search for a person’s name with terms that are offensive or disparaging. We made this change a while ago following feedback that Autocomplete too often predicted offensive, hurtful or inappropriate queries about people…Autocomplete isn’t an exact science, and the output of the prediction algorithms changes frequently. Predictions are produced based on a number of factors including the popularity and freshness of search terms..

If Yahoo! and Bing do not similarly omit offensive and disparaging results, that would explain why they predicted negative queries that Google did not, but it would not explain why Google would predict queries that disparage Trump, and Epstein published another article in September confirming that it did: particularly, the query “Donald Trump flip flops.” In that article, Epstein cited further experimental results indicating that undecided voters choose negative recommended queries fifteen times as often as they pick neutral recommended queries, and that can create a vicious cycle such that negative queries become more likely to be recommended.

When Google explained, “Autocomplete isn’t an exact science,” perhaps they meant it initially failed to recognize “flip flops” as disparaging (wanna buy some Donald Trump sandals?). However, Epstein who continued to monitor political bias in search results, reported that Google responded to his criticism by reducing their suppression of negative autocomplete results, thus producing a right-wing bias detrimental to Clinton at the time of the election (which Epstein seemed to think made things worse).

In short, the fact that users are so curious about surprising negative recommended queries, like “feminism is cancer,” makes the autocomplete features of Google, Yahoo! and Bing all drive traffic to extremist propaganda. Google had attempted to work around that legacy problem by blocking negative recommendations, but that workaround caused Epstein to accuse Google of bias. A more direct fix would be to remove our fascination with negative search results, and remove the evaluativism that causes election margins to get close enough for “fake news” and search engine bias to make a difference.

Standard Process to Address Ethics in Development

The IEEE Working Group developing P7000 – Model Process for Addressing Ethical Concerns During System Design has an interesting challenge when it comes to ethical concerns caused by legacy problems like evaluativism. On the one hand, it might describe a testing regimen to catch legacy problems before release. However, we have to wonder what tests would have allowed Microsoft and Google to prevent the criticisms they later faced with Tay, autocomplete, and manipulation of elections.

If it is impossible to describe a perfect test, perhaps P7000 could instead describe strategies that would allow developers to adjust when legacy problems eventually surface. For example, because Google’s design for autocomplete allowed Google to monitor autocomplete trends, they detected its tendency to predict offensive queries before Epstein did, and already had a workaround in place. Yet Google’s workaround did not satisfy Epstein—when encountering a legacy problem, there is often no workaround quite as good as fixing the actual legacy problem.

In addition to providing testing procedures and design strategies, P7000 should give engineers the same protection doctors enjoy. What ultimately protects doctors from becoming victims of obesity the way Microsoft and Google were victims of evaluativism is the way expectations are managed. We generally do not blame doctors for illness and death; we are grateful for whatever advice doctors can offer because we know that our bodies are doomed legacies. Likewise, P7000 must not shy away from admitting that our inherited systems of morality, justice, care, and governance are mortally ill. Malpractice is possible, of course, and standards should be created to prevent malpractice by technology developers, but until those standards are adopted and violated, legacy problems should be blamed on legacies, rather than on the innovators who discover them.

A Party to Recruit Corporantia

1009892593_d597a0608e_bImagine a party which goes like this:

  1. Guests: Upon arrival, each guest is given a bracelet with a letter and a color (e.g. for forty guests, there might be one red bracelet of each letter—A, B, C and D—two green bracelets of each letter, three yellow bracelets of each letter, and four white of each letter). Each guest must keep their bracelet for the duration of the game.
  2. Rooms: There is one room (or circle) per letter, and each guest is initially assigned to the room corresponding to his/her letter. At the beginning of the game, ensure that each room has exactly the right number of chairs for the number of guests assigned to that room.
  3. Winning: The goal of the game is to maximize dancing. When the music starts, each guest not “in poverty” goes to his/her assigned room. All guests with the letter corresponding to their assigned room dance.
  4. Entering Poverty: When the music stops, each guest must sit in a chair. If there are not enough chairs, then the guests assigned to that room must set an objective rule to decide who gets a chair. To make it objective, all criteria for the rule must come from the bracelets. For example, people cannot win chairs by being faster, stronger, or more aggressive. Instead, priority for a chair could go to people with red bracelets, or green bracelets, or the most common color, or the least common color, or the most common color among the impoverished, or to the color that didn’t get a chair last time (etc.). Anyone lacking a chair goes into “poverty”.
  5. Chair Movement: During each song, the host records a census of color and room assignment among those in poverty, then identifies two rooms at random. The room with more assigned people currently in poverty is the winner for that song and the other is the loser. The host, all people in poverty, and anyone sitting (not dancing) in a room other than the loosing room transfer one chair each from the losing room to the winning room.
  6. Prison: When someone from poverty takes a chair, the guests assigned to the losing room may optionally send that person to prison. Anyone sent to prison takes the chair to prison and sits in it until the end of the game. People in prison have no room assignment; they do not dance nor move chairs from room to room.
  7. Exiting Poverty: After chairs are moved, each person left in poverty flips a coin; those who get heads  leave poverty and become reassigned to the winning room (although they cannot dance if their bracelet doesn’t have the letter corresponding to their room assignment).
  8. Ending the Game: The songs get shorter and shorter. The party ends after a set number of songs (e.g. 20).

At the end of the party, the guests review the record of diversity among those in poverty. Were there times when the rules to decide who gets a chair changed? Why? How did guests feel about people who shared their color? How did they feel about people who shared their letter? How many people were dancing in the end?

This is an exercise you can use to raise awareness of how diversity impacts us. Rather than model diversity in a simplified way which implies that we should be blind to diversity, this exercise acknowledges that diversity comes in two kinds. Each room represents a social role, and the chairs in that room represent the number of positions available for that role. The letters and colors on the bracelets represent our diversity. Some elements of our diversity are relevant to social roles and others are not, yet both kinds of diversity can impact who loses social positions when there aren’t enough positions to go around.

“Stay-at-home parent” and “small business owner” are two examples of social positions that became dramatically less common at certain points in history. Participants in the exercise should ask themselves: How many such transitions do I expect to witness in my lifetime? Were any stages in the game reflective of modern life? What would it take to maximize dancing?

This game is rigged for evaluativism: Even though rule 5 always favors the room with the greater opportunity to improve, it writes-off the losing room entirely. Then what comes around goes around; no matter what players decide about who goes to prison and who goes to poverty, rule 5 rigs the game so that most people will not be dancing in the end.

Corporantia are players who want to replace rule 5 with a more subtle kind of chair-balancing that scientifically determines the number of chairs to move. They want to figure-out how many people were assigned each letter, and balance the distribution of chairs across all rooms so that the number of chairs in a given room matches the number of people with the corresponding bracelet. To implement such a rule in real life would relinquish unprecedented political and economic power to science. Those who propose such a shift can seem to be “playing god,” and it takes exercises like this one to build consensus.

Interdependent Meals and Post-Publication Peer Review

Here are two more things you can do to advance the management of GRIN diversity:Interdependent meal

  1. Host an interdependent meal, and
  2. Promote post-publication peer review of the GRINSQ valida-tion study

These opportunities arose from two practical efforts that have been underway for the last two and a half years:

  1. The development of a social movement against evaluativism
  2. The development of science to measure the impact of GRIN types and evaluativism in our world

 

The Social Movement and the Interdependent Meal

The idea of organizing a social movement against evaluativism was inspired by the history of racism. Evaluativism and racism have both existed for millennia; both are implicit biases; both became entrenched by shaping the design of social institutions. Management of racism was ineffective until a social movement was developed to overcome it. One might expect the same for management of evaluativism.

The movement against racism started in churches, and it seems appropriate for the movement against evaluativism to start in churches as well:

The suggestion that the church create a social movement against evaluativism was taken to Erin Hawkins, General Secretary of the General Commission on Religion and Race (GCORR). Based on her experience with race and the church, she suggested that the movement would need to be grassroots. Erin’s experience suggested that congregations are unlikely to address discrimination when the movement is created by a central administration like GCORR.

Therefore, a core team of clergy from across Wisconsin met once a month for about a year to plan an event, and produced a plan entitled “Christian Response to Evaluativism in Wisconsin“. The work of the core team included a great deal of discovery and invention (e.g. the plan includes a recipe for an interdependent meal). Perhaps most importantly, it found that responsible management of evaluativism requires resources lacked by typical congregations, so the movement cannot be built in a grassroots fashion. Central leadership must take responsibility to manage evaluativism.

A movement against evaluativism may be less likely to find institutional support from churches than from organizations which represent victims of evaluativism (e.g. child advocacy organizations or neurodiversity organizations) or from an association of organizational psychologists. For society to face the facts about evaluativism would shift social influence (and money) to groups of the latter kinds. Nonetheless, only churches can lead exploration of the theological dimensions.

 

The Scientific Movement and Post-Publication Peer Review

The social movement is expected to advance hand-in-hand with a scientific movement—scientific discoveries justify the social movement, and the social movement gathers the resources required to make discoveries.

Science needs a movement because the current quality of social science is poor like the quality of medical science was poor until about a hundred years ago. The first scientists to measure evaluativism and evaluative diversity (which they called “moral diversity“) supported evaluativism. The same was true of philosophers. Only recently have influential scientists begun to entertain evidence that evaluative diversity is hardwired and useful. Yet, even now, such science remains scattered by the division of scientific disciplines.

Given the current state of science, there is no central email address to which one might submit a hypothesis (like the GRIN model) or a measure (like the GRIN Self-Quiz) to be put on a waiting-list for testing. One must either run tests oneself or form relationships with particular scientists to convince them to run the tests.

In 2011, Chris Santos-Lang began discussing evaluative diversity with Ray Aldag. They met once a week until 2015. Ray encouraged Chris to begin testing the GRIN model via survey research. That research was completed in 2013. In addition to confirming that GRIN types could be discriminated among humans, it produced some rather shocking evidence:

  • Political affiliation aligns with GRIN type
  • Religious affiliation aligns with GRIN type
  • The career you end up in aligns with GRIN type
  • Whether you are accused of a crime (and probably whether you end-up in prison) aligns with GRIN type

This evidence implies that our political, religious, vocational and justice systems are not what we think they are, and it raises serious doubts about popular conceptions of freedom. To rally the scientific community to address this evidence, Chris submitted the research for peer-review and publication.

Why is it important to rally the scientific community? Eventually science gets too complicated for one person to advance alone. We would want to conduct twin studies, genetic tests, and brain imaging to work out the mechanisms through which the GRIN model manifests in humans. It takes many people to raise the funding and conduct all of the tests.

Chris submitted to ten peer-review processes and received a total of six blind reviews. None endorsed publication, yet none found any flaws in the research. Having confirmed that flaws in the research (if any) are not obvious, the research and peer review were published on figshare. Any flaws discovered in the future should be published via post-publication peer review at PubPeer. If you know anyone who could find flaws in the research (i.e. someone who conducts survey research), please encourage them to review it. Ray used the GRIN Self-Quiz to make further discoveries himself (e.g. described here), and we hope others will find it useful as well.

How to discover when you have the wrong goal

[SPOILER: This is not self-help.]

The short answer to the question, “How can I discover when I have the wrong goal?” is by gaining enough self-awareness to see where your goal came from. Before explaining how to do that, however, we must address the objection that it would be impossible to have a wrong goal.

This objection is well-represented by the 1963 song, “You don’t own me” by Madara and White. Here’s an excerpt:

Don’t tell me what to do
and don’t tell me what to say

I’m young and I love to be young,
I’m free and I love to be free,
to live my life the way I want,
to say and do whatever I please.

By associating ownership (a.k.a. “slavery”) with attempts by one person to change the goals of another, the title of this song implies that immaturity and ignorance are moral rights, that we ought to let people who have wrong goals blissfully believe that their goals are freely chosen and correct.

Grace, the singer who rerecorded the song in 2015, said “I know who I am and what I want to do, and this song speaks to that. It’s so important to go after what you want, to be strong.” I doubt she meant that she could not possibly learn anything about her identity and desires from new scientific discovery. Rather, I think she meant that it would be wrong to just sit around waiting for scientists to gather objective evidence regarding who you are and what you want. When we say it is important for people to have self-esteem, we may mean that it is important not to get stuck in the paralysis of second-guessing one’s goals.

So many self-help books advise us about how to achieve our goals, but assume that we have the right goals. Many specify particular goals that could be good except perhaps that a different goal should take priority. Building wealth, getting fit, improving relationships, changing the world—only one can take priority for a given person at a given moment. For example, for certain persons, the goal of building wealth might be wrong because it stands in the way of the right goal of improving their marriages (or vice-versa).

It is possible to claim that our goals are right by virtue of being selected. If you regret the goals you had ten years ago (e.g. to get drunk and hook-up), you could tell yourself that those choices were right for the person you used to be. We could look at less-developed societies who invested more in killing each other than in developing technology, and we could tell ourselves that killing each other was the right goal for them at that point in their development.

On the other hand, we could believe it is possible to make mistakes, to be manipulated, to lack self-awareness, to be immature, ignorant, and unsophisticated. This entails a sacrifice of self-esteem because the minute we believe someone had the wrong goal, we must realize that someday someone may criticize our current goals in the same way. But, surprisingly, that loss of self-esteem is not paralyzing. On the contrary, it is inspiring—it motivates us to seek greater self-awareness, greater freedom.

Many people fight—and even give their lives—for the sake of freedom, and that makes sense only if we believe it is possible not to be free. For example, some people fight for security, hoping to prevent fear from manipulating their loved-ones into shifting from a goal of good relationships to an “every-man-for-himself” goal of personal survival. Likewise, some people fight for health, hoping to prevent stress from manipulating their loved-ones into shifting from a goal of addiction-avoidance to a goal of escape. Our behavior demonstrates our belief that people can be manipulated, and therefore can have wrong goals.

The Strategy

This brings us to our strategy for discovering when we have the wrong goal. Our strategy is to determine where our goal came from—did it result from manipulation?

Notice how important it is to determine the origins of our goals through self-awareness: If I tell you that you have the wrong goal, won’t that manipulate you into choosing a different goal? Technically, it is possible that I might manipulate you into choosing the goal you would have chosen if not manipulated, so self-awareness isn’t strictly necessary. However, how can you trust me to be so benevolent? Only through self-awareness can you be sure that your goal is right.

On the other hand, completely independent self-awareness never happens. Whenever we calculate, we trust those who invented and taught mathematics. Whenever we think in language, we trust those who invented and taught that language. Whenever we use the Internet to research facts, we trust those who authored the claims, those who provided quality control, and those who secure the Internet.

Do people who teach math and language own us? Of course not! Many people teach the same math and language, so we do not rely on any particular teacher, and any teacher who teaches it incorrectly is likely to be discovered. Likewise, if someone tells us that getting drunk is the wrong goal, rather than complain that someone is trying to own us, we should be able to compare against advice from other sources. The fact that getting drunk is the wrong goal is common-knowledge like math and language. Things don’t get dicey until understanding the origins of our goals requires uncommon cutting-edge knowledge.

How can cutting-edge knowledge enter the mainstream? Since we can’t validate it on the basis that it is well-known, it has to be able to be validated in some other way—and the means to validation also need to be able to be validated. That’s a two-way street: On the one hand, new discoveries need to be presented in terms of experiments that can be replicated. On the other hand, we need a broad community to replicate experiments. It is not enough that elite scientists hold each other accountable; organizations which confront cutting-edge science (e.g. churches) also must develop and implement a capacity to test new discoveries.

This is a lot of work. Even elite scientists tend not to test each other’s discoveries in a timely fashion—they are more interested in making discoveries of their own. With so little replication being attempted, it’s no wonder discoveries are rarely published in ways that make replication easy. In fact, scientists seem inclined to make discoveries that would be difficult to replicate (e.g. by using special equipment). So we have a tax to pay. If we want to be able to discover when we have the wrong goals—if we want freedom—then we need to go to the trouble of building a social infrastructure that can move cutting-edge knowledge to the mainstream.

Progress so far

What happens when we apply this strategy? What happens when we build self-awareness by learning what is well-known and bringing cutting-edge knowledge into the mainstream? What do we discover about the origins of our goals?

We find that some goals are right because they are practical. For example, the goal to stay alive is practical, and that requires us to eat and sleep, so a certain amount of eating and sleeping are practical goals as well. If we aim to eat or sleep more than necessary, then our goal is no longer practical, and probably wrong.

The goal to adapt is also practical. Because adaptation occurs gradually as new configurations spread across a community, the goal to speed adaptation relies on the goals of increasing four other quantities:

  1. Rate at which novel configurations are produced (G)
  2. Bias for better configurations (N)
  3. Fidelity with which proven configurations are reproduced (I)
  4. Localization of reproductive networks (R)

The equation for rate of adaptation goes like this (where W represents how close the community is to perfection)

dW/dt = (G)(b-W) + (R)(I)(N)var(W)/W

The two terms in this equation may be thought of as “rate of adaptation through mutation” and “rate of adaptation through reproduction.” In order for adaptation through reproduction to occur, R, I and N must all be non-zero, so all three are practical goals. G is also a practical goal if the community is not so close to perfection that W>b. However, because adaptation takes place at the level of the community, it is practical for these goals to be assigned to different members of the community. Thus, it is good for us to have different goals, but it is not good for us to take our personal goals to such an extreme that we prevent the other goals from being pursued by other members of our community.

The four goals correspond to GRIN-types. You can apply the GRIN-SQ to confirm that these goals are distributed across our communities. Comparing the GRIN-SQ to other surveys which have biological correlates reveals that these goals are hardwired. The hardwiring could be tested more directly by including the GRIN-SQ in twin studies and studies with fMRI and EEG (etc).

The singer of “You don’t own me” pleads “…just let me be myself.” In order to allow people to discover which goals they are hardwired to pursue, we should calibrate biological instruments to measure our hardwiring just like we can measure blood type. Assuming enough people are hardwired for each goal, it will be most practical for each person to follow the goal corresponding to their current hardwiring. Thus, we need only to measure our own hardwiring and confirm that other members of our community have the other goals covered.

That allows us to confirm that certain practical goals are right, but whether or not we can have right goals that are not merely practical (i.e. goals which we choose freely) will be difficult to tell. For example, some people appear to chose goals corresponding to a GRIN type other than his/her natural type. Only recently have we uncovered evidence that such behavior is actually manipulated through neurochemistry triggered by certain engineered social situations.

When we see the practical origins of a goal, we can know it is right—it is what we have to do—but goals which have no practical origins might be found to be wrong. The way to test is to monitor the circumstances under which goals shift. If we find a circumstance (be it a chemical, a ritual, or interaction with a particular person) that shifts many people’s goals in the same way, then we have found a form of manipulation. Our ability to escape ignorance depends upon building a social infrastructure for such testing.

 

The process of achieving greater self-awareness, discovering our roles in our community and discovering sources of manipulation is a process in we should all share. Since we rely on each other to play different roles in our community, we are interdependent and it is in our best interest that we all avoid getting distracted by wrong goals. Some of us should design the research, others should critique the designs, others should collect the data, and yet others should test the replicability of the results. The process is expensive, but it is not a process any of us need implement alone.

At this point, the development of self-help guidance must include development of community leadership. The popularity of songs like “You don’t own me” implies that the general public is not yet prepared for that shift, but some of us are already discovering that many people have the wrong goals.

“Evaluative Diversity and the Board” published in Board Leadership

July-Aug 2016 Issue of Board LeadershipAn article published in the July/August 2016 issue of Board Leadership: Innovative Approaches to Governance presented the GRIN model using the same pictures found here, but acknowledged that two implications for governance also follow from the models of evaluative diversity presented in Predisposed, Teamology and The Righteous Mind. A common theme runs through all four models: “no one can be all things.”

The article was paired in this issue with “New Ways of Looking at Democracy,” an edited extract from Brett Hennig’s forthcoming book The End of Politicians. Hennig’s article suggested that “democracy” originally referred to systems in which leaders were selected at random (what he calls “sortition“) and that selection of leaders through election or appointment has since degraded democracy.

Hennig pointed out that random selection increases the perceived legitimacy of leaders because it makes leaders more similar to the communities they lead. Randomly selected leaders would not be mostly male or largely committed to donors, political parties, and political reputations.

This argument for sortition is undermined by the first implication of evaluative diversity: It would be naive to attempt to protect diversity by selecting representatively diverse leaders, since people of certain evaluative types are less likely to represent others who share their own type. In other words, the discovery of evaluative democracy disconfirms the theory behind representative democracy.

Using the GRIN model as an example, when two people with the same goals, loyalties and information apply negotiator evaluation or institutional evaluation perfectly, they will vote in exactly the same ways, so they will perfectly represent each other. However, the more perfectly two people apply gadfly evaluation, the less likely they are to reach the same conclusions. Thus, as we approach greater evaluative ability and alignment of goals, loyalties and information, a board with five members of each type would effectively give five times as much vote to each negotiator or institutional board member as it does to each gadfly board member. The system would be rigged against natural gadflies.

Democracy is possible, but representative democracy is not.

To avoid systematically handicapping citizen of particular evaluative types, the governance system must somehow resolve disagreements without having to put issues to a vote. The primary job of leaders should not be to vote, but rather to resolve disagreements through such mechanisms as evidence, empathy, creativity, and humility so that votes need never be taken. Such dispute resolution is democratic because it involves the entire community, and boards should involve the entire community, rather than expect to find all necessary ability within itself.

Hennig cites Scott Page’s, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies as providing evidence that “diversity trumps ability when solving problems and producing innovative ideas.” Both articles agree with Page that diversity is valuable, but the evaluative diversity article claims that the better way to protect that diversity is through monitoring (as one would protect diversity in an ecosystem) rather than merely shifting power among leaders. This is the second implication of evaluative diversity for governance: Boards should attain the benefits of evaluative diversity by extending current financial accounting practices to include monitoring of organizational culture.

Boards already require organizations to measure and report their income, debt and assets on a regular basis. If there are dramatic changes in these measures compared to the previous period or deviations from expectations, then the board sounds an alarm—management will be replaced if it cannot explain/correct the discrepancy. This is how managers are held accountable to serve the organization well.

But an organization is not merely a money machine—a healthy organization brings together people of diverse evaluative types, so good management must also include assuring that none of the different kinds of contributions gets systematically blocked. Such assurance would produce a balance between evaluative types, and shifts in that balance could be measured in terms of shifts in cultural variables such as the organization’s unity, consistency, creativity, and competitiveness. If there are dramatic changes in these measures compared to the previous period or deviations from expectations, then the board should sound an alarm just as it would for shifts in financial measures.

By pairing the two articles, Board Leadership highlighted what happens when there is not enough science guiding governance. First of all, the resulting governance-failure produces frustration which is well-documented in the sortition article—only genuine frustration with current governance could justify replacing elected/ appointed leaders with randomly selected leaders. Second, even though the frustration is caused by a lack of science, it does not necessary motivate increased investment in science. Hennig and I personally discussed the opportunity to protect diversity through monitoring two months before we wrote our articles, yet our articles still offered contrasting recommendations. Hennig offered no argument for or against the use of monitoring to protect diversity, and no one forced him to address that possibility . Thus, the lack of science produces not only frustration, but also permits confusion about how to resolve the frustration.