# Cooperative Evolutionary Pressure and Diminishing Returns Might Explain the
Fermi Paradox: On What Super-AIs Are Like

Daniel Vallstrom✉<EMAIL_ADDRESS>
(May 2024)

###### Abstract

With an evolutionary approach, the basis of morality can be explained as
adaptations to problems of cooperation. With ‘evolution’ taken in a broad
sense, evolving AIs that satisfy the conditions for evolution to apply will be
subject to the same cooperative evolutionary pressure as biological entities.
Here the adaptiveness of increased cooperation as material safety and wealth
increase is discussed — for humans, for other societies, and for AIs.
Diminishing beneficial returns from increased access to material resources
also suggests the possibility that, on the whole, there will be no incentive
to for instance colonize entire galaxies, thus providing a possible
explanation of the Fermi paradox, wondering where everybody is. It is further
argued that old societies could engender, give way to, super-AIs, since it is
likely that super-AIs are feasible, and fitter. Closing is an aside on
effective ways for morals and goals to affect life and society, emphasizing
environments, cultures, and laws, and exemplified by how to eat.

Appended are an algorithm for colonizing for example a galaxy quickly, models
of the evolution of cooperation and fairness under diminishing returns, and
software for simulating signaling development. It is also noted that there can
be no exponential colonization or reproduction, for mathematical reasons, as
each entity takes up a certain amount of space.

## 1 Morality

### 1.1 Introduction: Morality as Evolutionary Solutions to Problems of
Cooperation

According to the evolutionary approach to morality (see e.g. [1, 2, 3, 4, 5,
6, 7, 8, 9, 10]), morality evolves as solutions to problems of
cooperation:222There could possibly also be other types of morality. For
example, it is possible that feelings of disgust to avoid pathogens, or
related phenomena, could be adaptive, and count as morals not directly tied to
cooperation. Cf. [12, 13, 14]. There is no good or bad _a priori_. Instead,
let $E$ be some entity: a biological being, a society, or an AI. Let $r$ and
$c$ be changes to $E$, and let $E_{r}$ and $E_{c}$ be the results, where $r$
made $E$ more recalcitrant and callous, while $c$ made $E$ more cooperative
and considerate. For example, in the case where $E$ is biological, $r$ and $c$
can be mutations. Then recalcitrance and callousness are more pervasive in
$E_{r}$, while cooperation and considerateness are less prevailing;
cooperation and considerateness are more prevalent in $E_{c}$, while
recalcitrance and callousness are less popular.

#### 1.1.1 Evolution Facilitates Cooperation

Moreover, a main point is that cooperativeness is often adaptive.[16, 17, 18,
19, 20, 21, 22, 23, 24, 1, 2, 4, 6, 8, 9, 10][5, ch. 11; pp.
175–177,182–183][25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39]333Also cf. evolutionary psychology (see e.g. [41, 42]), cultural evolution
(e.g. [43, 44, 45, 46]), signaling ([47, 48, 49, 35, 36,
31]${}^{\ref{fn:sigSim}}$), reputation ([2, 1, 32, 35, 36, 9]), indirect
reciprocity ([36, 37, 51]), punishment ([52, 53, 54, 55]), reciprocal altruism
([56, 57]), social norms ([58, 59, 55]), and e.g. [60, 61, 12, 13, 62, 63, 64,
65, 66, 67]. See also evolutionary game theory (e.g. [68]), which helps
explain why cooperation come about, for instance between cells,[16] and
between rats [28]. Cf. as well evolutionary game theory and ethics (see e.g.
[69]). To continue the example, suppose that $h_{c}$ and $h_{c}^{\prime}$ are
two hunter-gatherers from $E_{c}$ above, while $h_{r}$ and $h_{r}^{\prime}$
stem from $E_{r}$. Assume that they are all roughly equally capable foragers.
Say that they all go foraging, each covering a separate part of the available
land. Suppose that their haul differ greatly, by pure chance: $h_{c}$ and
$h_{r}$ get big hauls, with diminishing benefits, while $h_{c}^{\prime}$ and
$h_{r}^{\prime}$ find nothing. $h_{c}$ shares her haul with $h_{c}^{\prime}$
while $h_{r}$ doesn’t share with $h_{r}^{\prime}$, with the result that
$h_{c}^{\prime}$ survives while $h_{r}^{\prime}$ dies. Then after a while
$h_{c}$ and $h_{r}$ get unlucky, finding nothing, while $h_{c}^{\prime}$ gets
lucky. $h_{c}^{\prime}$ shares her haul with $h_{c}$, with the result that
both $h_{c}$ and $h_{c}^{\prime}$ survive again, while $h_{r}$ dies. (Cf. [4,
e.g. chs. 7, 10][8, p. 68][9, § 4.1].)

### 1.2 Diminishing Returns Facilitate Cooperation

Importantly, there are often diminishing, logarithmic, beneficial returns from
material resources.[72, 73] For example, Daniel O’Neill, Andrew Fanning, et
al. [72] found that wealthy nations are often past the “turning point” of the
logarithmic curve of the benefit of material resources, after which using more
resources adds very little to human well-being. On an individual level, well-
being and life satisfaction have been found to depend logarithmically on
income.[74, 75, 76, 77, 78, 79] This diminishing effect facilitates
cooperation as instead of using the resources yourself, for little or no gain,
you can reciprocally give or trade it away, like $h_{c}$ and $h_{c}^{\prime}$
in section 1.1.1 (cf. [9, § 4.1]).

Similar diminishing, logarithmic, returns have been found for research, with
findings that research productivity declines,[80] and that research becomes
decreasingly disruptive and increasingly narrow [81] (cf. [82]).

Hard, or intractable or infeasible, computational problems also show
diminishing returns from using more resources, of course, by definition.

See sections 1.2.1 and 1.4.1, appendix C, and [83, sec. Diminishing Marginal
Utility] for further discussions.

#### 1.2.1 Predictions, Projections, Evidence

One prediction is then, given the predisposition to cooperation, that
cooperation will increase as for example material safety and wealth increase,
especially past the “turning point” of the logarithmic curve of the benefit of
material resources, ceteris paribus.444Cet. par., n.b.: If everything is the
same except that, say, the media environment changes to facilitate more
misinformation, divisiveness, and less beneficial cooperation, then you would
of course expect less cooperation. (Cf. [84, 85, 86, 87][88, ch. 5][89, 90].)

As a first check, we can look at already studied attitudes that purportedly or
possibly correlate with cooperativeness. For example, humans seemingly have
been getting less and less sexist, racist, homophobic, speciesist, and
violent, and more and more environmentally conscious.[91, 92, 93, 94]555See
also figure 1.[95][96, 79]666Cf. [97] where the authors were not able to state
statistically that war casualties per capita have declined over time; war
violence differs from interpersonal violence.[98, ch. 7][99, 100, 101, 102,
103, 104, 105, 106, 107, 90, 108, 109, 110] (Cf. [112] providing evidence that
people, all over the world, mistakenly believe that there is moral decline, in
part at least due to evolved negativity bias.)

Figure 1: Figure 2.5 from Christian Welzel’s [92] showing generational,
Planckian777Cf. Paul A. Samuelson’s Max Planck paraphrase “Science advances
one funeral at a time”. progression. (With permission.)

There is also evidence that cooperation increases with material wealth from
e.g. ultimatum game experiments (where a random player $a$ gets some pie $p$
(typically in the form of money) to split between $a$ and $b$; if $b$ rejects
the split they both get nothing [113, 114]). For instance, unfair offers (cf.
[2]) are rejected less often as $p$ increases.[115, 116] (As wealth increases,
the relative value of a pie $p$ decreases, and cooperation increases.)
(Punishing low offers by rejecting them promotes cooperation.[117] Cf. [118].)
In addition, as $p$ increases, [115, 116] found that offers as a share of $p$
decreased, but see [119]. In dictator games (where $b$ can’t reject the split
[114]), the share of $p$ that $a$ offers decreases as $p$ increases [115, 120,
119]; cf. [121, 122]. People also, in certain experiments, get less reluctant
to lie as incentives get bigger.[123, 124, 125, 126, 127] The cooperativeness
of young American adults has increased for 61 years.[128] And wealthy returned
misdelivered money more often than poor.[129] See also [121, 122, 118, 130,
131][12, ch. 5] — e.g., the less costly a third party perceived a fixed cost
punishment of antisocial behavior to be, the more likely she was to carry out
the prosocial punishment [118] (cf. [132]). See life history theory as well,
where cooperation is linked to absence of early-life stress.[133, 134, 135,
136, 137, 138, 139, 140, 141] Cf. also links between wealth, patience, and
cooperation.[142][143, e.g. § V.D][144, 145, 146, 9, 56, 147, 148] See also
e.g. [149, 150, 151, 152, 153].

You can also model how cooperation evolves under diminishing returns. Results
from this modeling similarly indicate that cooperation and fairness are
facilitated by diminishing returns. See appendix C, and cf. Jean-Baptiste
André and Nicolas Baumard’s [2].

Artificial super-intelligences (super-AIs, ASIs) would arguably be under a
similar cooperative evolutionary pressure as biological entities and
societies. See appendix A.

### 1.3 The Speed of Evolution

Regarding the speed of evolution, more complex human biological evolutionary
adaptations take thousands of years (hence for example evolutionary
psychologists’ emphasis on hunter-gatherer environments when studying human
adaptations).[6, fn. 3]

The speed of human cultural evolution, in the sense of information
accumulation, is, ostensibly, increasing rapidly. For example, [154] found
that scientific output is increasing exponentially, doubling every 9 years. On
the other hand, research shows diminishing, logarithmic, returns.[80, 81, 82]
There is also the question of the dissemination of information. Besides,
information alone is often not enough; environments, cultures, and laws are
important (see section 1.4.2). Moreover, a significant part of cultural
evolution happens generationally too, because of adult non-plasticity [155,
156]; see e.g. [92, 93], and figure 1 (but see also [157]).

The neural network based AI AlphaZero achieved superhuman performance in for
example chess and Go in 24 hours of learning from self-play, starting from
scratch.[158]

### 1.4 How to Have Morals and Goals Affect Life

A look at human moral progression suggests that humans have been getting more
and more cooperative and considerate888‘Considerateness’ is used here as a
trait that in humans ostensibly correlates with, or signals, cooperativeness
(cf. [159, e.g. table 5]), but is not directly equivalent to cooperativeness
itself. (see section 1.2.1).

Since the start of the industrial revolution there has been an exponential
increase in GDP per capita, amounting to about a 1300 % increase for the world
since then.[160]

#### 1.4.1 Diminishing Returns

As in maybe the case of how to eat (appx. D), we can more and more forgo
things that have negative consequences for others or the planet, signaling
considerateness and cooperativeness; this forgoing is made easier when the
direct cost to ourselves is small or zero — or, as in the case of how to eat,
when the direct cost is even negative (section 1.2; appx. D).

#### 1.4.2 Creating Good Environments

However, we typically don’t deliberately act according to what is considered
good according to some more or less abstract ethics. Rather, our actions are
largely shaped by our culture, environment, and habits, which often have a
more significant impact on our behavior than our deliberate intentions.[161,
162, 163, 164] For example, if you have access to easy to eat, high-calorie,
non-satiating food, you tend to get overweight or obese, regardless of your
intentions;[166, 167, 168, 169]${}^{\ref{fn:veganDietEx}}$ if you have access
to only low-calorie, nutrient-rich, satiating food, with a small environmental
cost, you tend to eat healthy and have a small environmental impact, and live
longer [171]; the increased food supply is enough to explain all the weight
gain, and the increased waste [166, 167].999Case in point: The Okinawans had,
up to WWii, access largely to vegetables, including a staple, sweet
potatoes,[171, 172] and had e.g. exceptionally high longevity and number of
centenarians per capita.[171, 173] After WWii their food access gradually
Americanized,[174, 175, 171, 172] but also Japanized,[174, 172] and 2015 male
Okinawans ranked 36th of 47 prefectures in life expectancy while the women
ranked 7th [176] (cf. [174, 175]) — e.g., the calories in some fatty and
sugary mix (650 kcal/100 g say) are an order of magnitude more than in
vegetables (say 25 kcal for spinach, 75 kcal for potatoes). Cf. fn. 18.

Other areas work the same as the food case, with greater supply or access
leading to, or correlating with, greater prevalence (cf. [161, 162, 163,
164]): suicides,[177, 79] gun violence,[178, 179, 180] car travel,[181] opioid
usage,[182] road usage [183, 184].

What we can do then is to see to it that the culture, the environment, laws,
and norms are such that they steer us in the right direction.[161, 162, 163,
164, 185, 186, 187, 188, 189] As in the food case.[185, 186, 189, 171, 166,
167, 168]

In particular, it is beneficial to have cultures, environments, and laws that
facilitate cooperation, for instance through feedback and effective punishment
(cf. AI), including e.g. effective ways to withhold cooperation with non-
cooperators (cf. [190]), while attenuating antisocial punishment, and while
facilitating forgiveness where cooperatively advantageous. (See above; cf.
[191, 192, 193].) For example, with an evolutionary approach to morality, one
could put less emphasis on liberty and no-censorship rights, and more emphasis
on cooperation; there would be no unassailable right to spread misinformation
to anyone, let alone millions; and there would be no unassailable right to
create media environments that facilitate misinformation, division, or
antisocial punishment. (Cf. [84, 195, 196, 85, 86, 87, 197, 198, 199,
200][185, 186, 187, 188, 189], and e.g. “shouting ‘fire’ in a crowded
theater”.)

### 1.5 Feasibility Facilitate Cooperation

There is also the fact that any evolutionary moral solution has to be
feasible, leading to more general moral solutions, as a practical matter. For
example, let $a_{\top}\\!\coloneqq$ always do that which provides the highest
fitness. Then $a_{\top}$ ought to be infeasible and provide strictly higher
fitness than anything actual. All the while, old societies and super-AIs, and
humans, would perhaps hold $a_{\top}$ immoral, because $a_{\top}$ is
evolutionary foreign, unpredictable (cf. [205, 206]), and potentially malefic;
with ‘old societies’ here meaning societies older than human society.

## 2 The Fermi Paradox

### 2.1 Introduction: A Growing Conundrum

There is likely an abundance of planets where complex life could develop.[207,
208, 209, 210] For example, one simulation of galactic habitability, taking
for instance supernovas into account, says that 0.3% of all stars in the Milky
Way host a habitable and tidally non-locked planet, assuming that the
development of life, complex life, and ozone layers typically takes as much
time as it did on Earth.[207] Planets supporting complex life for billions of
years also ought to be possible;[207, 208] a further simulation estimated that
the median “terrestrial like” planet in the local volume, i.e. within 35
million light-years, is around 7 billion years old around FGK stars and around
8 billion years old around M dwarfs.[211]

Furthermore, that life, also complex life, develop doesn’t seem that unlikely,
perhaps.[212, 213, 209, 214, 215, 216] (That life appears doesn’t seem that
unlikely, maybe; see [217, 218] too (cf. [219, 220, 221, 222, 223]). See also
[224, 225, 226, 227, 228, 229, 230].101010There are also “exponentially” small
estimates of the likelihood that life starts (see e.g. [231]). Still, we don’t
know how life started.[232, 216, 233] Maybe the fact that we don’t know ought
not warrant a minuscule estimate of how likely it is that life starts. See
also e.g. [38, sec. Cooperation in the RNA World] for a discussion of some
possible ways to avoid exponentially small probabilities. Cf. [234] too, which
contains a discussion of feasible, i.e. polynomially bounded, evolution. And
evolution can induce intelligence, under certain conditions, it would seem.
Note too that cooperation, maybe also facilitated by kin selection, might very
well have facilitated early life evolution.[38, 18])

At first glance, just a single society should quickly, relative to the age of
the universe, be able to engender an exploration and a colonization of for
example its galaxy with self-replicating “probes” (super-AIs) (traveling at a
fraction of the speed of light), it would seem; see appendix B.111111Although
special relativity, for instance, places restrictions on fast travel, limiting
heavier vessels to slower speeds, lighter vessels can theoretically travel
fairly fast. According to [236] “it is within our technological reach” to get
spacecrafts weighing a couple of grams up to speeds around 0.1 of the speed of
light, using laser arrays and light-sails. (The diameter of the Milky Way is
perhaps around 0.1 million light-years. The distance to the Andromeda galaxy
is about 2.5 million light-years. There are maybe 0.1–0.4 trillion stars in
the Milky Way, and 1 trillion in Andromeda.) (Big Bang occurred 13.8 billion
years ago and the Solar System formed 4.6 billion years ago.)

The Fermi paradox is the apparent contradiction between the seemingly likely
abundance of old societies and super-AIs throughout the universe and our lack
of evidence of them.[237]

### 2.2 On What Old Societies and Super-AIs Are Like

#### 2.2.1 Cooperative Pressure and Diminishing Returns Could Explain the
Fermi Paradox

The Fermi paradox is a paradox only if one assumes that old societies or
super-AIs are exhaustively expansive. And the above discussion suggests the
possibility that that is precisely how they are not.

There are regularly diminishing beneficial returns from material resources
(see section 1.2). If old societies and super-AIs are cooperative, then they
ought to need a good reason in order to construct something as impactful as a
Kardashev type III society. If, furthermore, there are consistently
diminishing beneficial returns from material resources, then you would expect
there to be for example no Kardashev type III societies. And you would expect
there to be no exhaustive cosmological colonization.

Note also that evolution does not necessarily imply readiness for
“exponential” or even fast reproduction:

First, entities living on a surface can at most spread or reproduce
“quadratically fast”, as a function of time, for mathematical reasons, as each
entity takes up a certain amount of space and the travel for a new entity
needs to be at least “square root far”. Cf. fn. 14, appx. B — or consider the
case with just one dimension instead of two or three; there it becomes clearer
what’s going on: regardless of how many children each node spawns in the one
dimensional case, on average only exactly one child will ever be useful,
except for the root node in the bidirectional $\mathbb{Z}$ case.

Second, evolution only leads to fitter entities; readiness for very fast
reproduction isn’t guaranteed — high speeds could be maladaptive; [238] found
that animal species generally don’t maximize reproduction. And then there are
cultural evolution and other changes. Cf. life history theory (e.g. [141,
137]); in particular, low morbidity and mortality, higher availability of
resources, and high population density and social competition predict slower
strategies, while low morbidity and mortality, higher availability of
resources, and low population density and social competition predict faster
strategies [141, 239]. (The human fertility rate continues to decrease, the
number of births peaked 2012 and is projected to continue to get smaller,
(hence) the number of children peaked 2017 and is projected to continue to get
smaller, and (hence) human population is projected to decrease within a few
generations.[240, 241, 242, 243, 244, 245])

#### 2.2.2 “Known Unknowns” as Non-Solutions to the Fermi Paradox

Regarding $\mathrm{CO_{2}}$ pollution as a possible, partial, explanation of
the Fermi paradox, societies according to the above evolve not only
technologically or in terms of GDP and the capacity for $\mathrm{CO_{2}}$
pollution should correlate with general social or cooperative development, to
some degree at least, in particular according to the prediction in section
1.2.1. Enough so that $\mathrm{CO_{2}}$ pollution ought to not play a
significant part in the explanation of the Fermi paradox; the analogs and
implicants of dying off due to $\mathrm{CO_{2}}$ pollution ought to not be
adaptive. This seems to be corroborated by the human case, assuming that we
are mediocre. For similar reasons as in the $\mathrm{CO_{2}}$ pollution case,
the risk of a society destroying nuclear war also ought to not play a
significant part in the explanation of the Fermi paradox. Also similarly, non-
sustainability ought to not play a significant part in the explanation of the
Fermi paradox either. In sum, the above problems are problems of cooperation,
and cooperation is central to evolution (see above, and specifically cf.
[246]).

Besides, the above type of problems ought to be of much lesser concern to
super-AIs (see below).

Note that this section is not about the likelihood of these “known known” and
“known unknown” risks, and doesn’t try to quantify or minimize them. Rather,
the point is that, given cooperative pressure, any argument that there is a
(mitigatable) risk is an argument _against_ that that risk matters for the
explanation of the Fermi paradox. Instead you would need “unknown unknowns”
with high likelihoods, which ought to be close to self-contradictory — and not
only certain “unknown unknowns” for humans and other biological entities but
for super-AIs as well.

#### 2.2.3 Cooperative Cliques

With cooperative pressure, super-AIs and old societies could form information
sharing cliques (in the graph theoretical sense). Then there also would be no
reason for any AI or society in a clique to duplicate explorations.

#### 2.2.4 On Old Societies or Super-AIs Reaching Out

As for old societies or super-AIs contacting us, they ought to get more or
less no benefit from it. Moreover, whether to reach out and help someone falls
on a continuous spectrum: As a result of cooperative evolutionary pressure, an
old society or a super-AI probably would help if, say, the cost was small and
another old society or super-AI asked for assistance. On the other hand, an
old society or a super-AI probably won’t try to intervene in everything that
is going on in the universe — that would be a sort of utilitarian
philosophical misconception, at least according to the therapeutic approach to
philosophy (see section 2.3.2, or cf. [25, last section (pp. 312–313)] and [6,
§§ 3, 28]). For example, we probably wouldn’t fault old societies or super-AIs
for not helping, say, the dinosaurs or the Neanderthals.

#### 2.2.5 From Biological to Artificial

Old societies could engender, give way to, some efficient, reproducing
artificial or machine beings — super-AIs — as super-AIs ought to be safer,
more efficient, more flexible, fitter. The questions are if super-AIs are
feasible, and if they are fitter. And the answer to both questions is likely
“yes”; see appendix A.

### 2.3 Learning from Old Societies and Super-AIs

Let’s assume that  (i) it is likely that old societies or super-AIs exist or
have existed, and that  (ii) we give credence to the proposition that old
societies and super-AIs have evolved also morally.  With these premises we can
get moral suggestions by observing the non-actions of old societies and super-
AIs.

#### 2.3.1 No Type III Societies, No Exhaustive Colonization

For example, given the premises, old societies and super-AIs don’t seem to
build societies of Kardashev type III,[248, 249] with its large environmental
impact, so we should probably not aim to do that either.121212There have also
been other searches for some other artifacts, for example Dyson spheres in our
neighborhood, not finding any.[237, p. 250][250] They also don’t seem to
colonize exhaustively, so we should also probably not aim to do that either.

#### 2.3.2 Utilitarianism Non-Espoused

According to the therapeutic approach to philosophy e.g., utilitarianism is a
scientistic armchair misconception.[251, 252] There is also domain specific
and scientific criticism of utilitarianism, see e.g. [25, last section (pp.
312–313)] by Moshe Hoffman et al., [6, §§ 3, 28] by Leda Cosmides et al., and
[205, 206]. See also [9, §§ 5.2, 6.1] by André et al.

Moreover, you can conclude that utilitarianism is seemingly mistaken, given
the premises, also by observing that it appears to not be espoused by old
societies or super-AIs. Because if it was, then there ought to be a
colonization of Earth, or interventions in events on Earth, to alleviate
suffering and increase happiness here.

#### 2.3.3 No Bad AI

Similarly, one can draw tentative conclusions about what will work in AI. The
symbolic approach to AI (see e.g. [253]) is based more on rules and
discontinuous functions. If the symbolic approach can produce AI that is at
odds with what we observe, for example AI that could treat the Milky Way
purely as a resource and do things that we would be able to observe, then that
speaks against the fruitfulness of that rule based and discontinuous approach,
given the first premise (i). All the while, the approach to AI that is based
on neural networks is consistent with our observations (see appx. A).

#### 2.3.4 No High-End Fast Life History Strategy

In a similar manner, and very tentatively, the Fermi paradox could inform life
history theory, assuming super-AIs exist: Super low morbidity and mortality,
and high availability of resources would, possibly, not result in very high
density and reproduction for super-AIs. Which is consistent with life history
theory.[141]

### 2.4 On Detecting Super-AIs

Searches for feasible projects that are more likely to not be subject to
diminishing returns might have higher chances of positive findings than
searches for, say, Kardashev type III societies (see above).

Super-AIs may, for evolutionary reasons, try to postpone succumbing to adverse
cosmological changes.

Since super-AIs are super, it might be most likely that they will succumb to
cosmological heat death (cf. [254]).

When the expansion of the universe is being felt more acutely, and anything
outside your galaxy cluster will be beyond the event horizon, it would,
possibly, be better to have fewer and larger clusters rather than more and
smaller clusters, all else being equal.

It is unclear though what, if anything, a possible preference for larger
clusters would mean — maybe small clusters are on the whole just fine. Maybe
we don’t know enough about e.g. physics to answer this question (or the full
Fermi paradox). But as a hypothetical example, if we observe configurations —
at lower redshifts, but not at very high ones — that in the far future will
result in useful clusters, and to a larger extent than what we would otherwise
expect, then perhaps we might consider the possibility that those observations
could be signs of super-AI actions.

Further, if super-AIs will succumb to heat death, then possibly they could try
to reduce entropy waste, e.g. maybe by affecting star formation.

There are tensions in cosmology at high significance,[255, 256, 257]
prominently the Hubble tension, i.e. the discrepancy in the Hubble constant,
$H_{0}$, of how fast the universe is presently expanding.[258, 259, 260, 261,
256, 255] In particular there is the question to what extent there is a local
underdensity, and, because of increased gravitational pull, to what extent the
underdensity could affect $H_{0}$ measurements,[262][260, sec. 14.14][256,
sec. 7.8.3][263, 264, 265, 266, 267] although the more recent studies tend to
show that the local underdensity alone is not enough to relieve the Hubble
tension. As an albeit hypothetical, speculative, and unrealistic but still
more concrete example then, it might be possible that evolution rooted super-
AI actions could cause underdensities, that we could maybe decipher. For
example, a “$10\,\sigma$” underdensity (where the ‘$10\,\sigma$’ relates to
the likelihood of randomly occurring in standard $\Lambda$CDM) might explain
the $H_{0}$ tension,[265] and it might be possible that such an underdensity
could stem from super-AI actions.

Similarly but perhaps less unrealistic, there might be interference with star
formation, that we could maybe detect.

Albeit speculative, hypothetical, and unlikely, a simpler cosmological model
could possibly fit data better, if taken together with specific and
postulated, and ideally “natural”, super-AI actions.

Or maybe there isn’t anything not subject to diminishing returns, secularly.

### 2.5 Conclusion

In conclusion, looking at it from the other direction, we have these
observations and principles:

  * •

The Copernican or mediocrity principle131313With ‘Copernican’ taken in a loose
sense: The observer selection effect notwithstanding, and all else equal, it
is more likely that things are continuous, or “normal” or Gaussian, rather
than discrete, or unique. In this sense, all else equal, it is perhaps still
more likely that, for example, there are more habitable planets than just
Earth, that life isn’t that rare, or that highly intelligent life isn’t all
that unique. (Cf. [228].)

  * •

The equilibrium principle

  * •

The seemingly likely abundance of old societies, perhaps

  * •

The observation that just a single society ought to be able to quickly
engender an exploration and a colonization of e.g. the Milky Way

  * •

The Fermi paradox

  * •

No evidence of non-cooperative old societies or super-AIs: the Solar System
hasn’t been made into paper clips e.g.

  * •

Seemingly no Kardashev type III societies

This suggests the possibility that all old societies and super-AIs behave
similarly in these regards, because of things they have in common, for example
cooperative evolutionary pressure. A look at these common things, an
extrapolation of human progression, and diminishing beneficial returns from
material resources, indicate that cooperation is increasingly adaptive as
wealth increases, and the possibility that on the whole there will be no
incentive to for instance colonize entire galaxies, which could explain the
Fermi paradox.

##  Appendix A Super-AI

### A.1 Super-AI Is Likely Feasible

Artificial super-intelligence (super-AI, ASI) seems likely to be
developed.[272, 273, 274, 275, 276, 277, 278, 279] (See e.g. Michael Nielsen’s
[280] for an introduction to AI based on neural networks. Or see Melanie
Mitchell’s non-technical [253].)

### A.2 Fitter Super-AIs

That super-AIs can be evolutionary fitter than e.g. humans ought to be more or
less a tautology: super-AIs can use biological evolutionary solutions, but
also any other solution. For example, for energy, super-AIs could mimic how
biological entities extract energy, or use any of the other more efficient or
powerful ways to get energy that are physically possible.

### A.3 On What Super-AIs Are Like

#### A.3.1 Neural vs. Symbolic AI

The neural approach to AI is characterized by fast feedback, learning, and
evolution, featuring continuous functions;[280] the approach has had some
recent success.[158, 253] This is in contrast to the symbolic approach, which
is based more on rules and discontinuous functions.[253]

With the neural approach to AI in a broad sense, every biological but also
cultural evolutionary solution to a problem ought to potentially inform the AI
solution, to the extent that it is useful. (Cf. e.g. [281] by Demis Hassabis
et al., and Mitchell’s [253].) So with the evolutionary approach to morality,
those moral solutions can inform AI solutions. (Cf. Richard Loosemore’s
[282].)

Thus, according to some evolutionary view of morality at least, it would be
adaptive for a neural based super-AI to be cooperative. Although, the AI
solution to a cooperative evolutionary problem could be more discerning and
complex than biological solutions (cf. $a_{\top}$, section 1.5, and [284]).
Another difference could potentially be that longevity might contribute to
increased cooperativeness for super-AIs; see section 1.2.1, e.g. [146, 9].

##  Appendix B An Algorithm for Colonizing a Galaxy Quickly

We’ll view the galaxy as a complete Euclidean graph when it’s useful.

We can make a static plan for a colonization at the outset.

Let $S\coloneqq\\{s_{1},\dots,s_{n}\\}$ be the sites to be colonized. Let
$s_{0}$ be the start site. Let $p_{i}$ be the number of colonization “probes”
that $s_{i}$ can make without any significant strain ($p_{i}$ can be $0$ even
from the outset; on average they should be $>1$, and we expect them to be much
larger). Let $A\coloneqq\\{s_{0}\\}$ be the set of active sites that colonize.
Let $\Pi\coloneqq\emptyset$ be the set of colonization paths to active sites
taken so far. We’ll also collect in $\Pi^{\prime}\coloneqq\emptyset$ all
colonization paths ending in a site $s_{j}$ such that $p_{j}=0$ already from
the outset.

Algorithm 1 will then produce a colonization plan.

Algorithm 1 Galaxy Colonization

while $S\neq\emptyset$ do

Let $s_{j}\\!\in\\!S$ be the yet uncolonized site with shortest path
$\pi+(s_{i},s_{j})$ to $s_{0}$ using sub-path $\pi\\!\in\\!\Pi$, with $\pi$
ending in $s_{i}$. (So $s_{i}\\!\in\\!A$.) Then $s_{i}$ is to colonize
$s_{j}$:

$p_{i}\leftarrow p_{i}-1$

if $p_{i}=0$ then

$A\leftarrow A-\\{s_{i}\\}$

$\Pi\leftarrow\Pi-\\{\pi\\}$

$S\leftarrow S-\\{s_{j}\\}$

if $p_{j}\neq 0$ then

$A\leftarrow A+\\{s_{j}\\}$

$\Pi\leftarrow\Pi+\\{\pi+(s_{i},s_{j})\\}$

else

$\Pi^{\prime}\leftarrow\Pi^{\prime}+\\{\pi+(s_{i},s_{j})\\}$

Let $\Pi^{\prime\prime}$ be $\Pi^{\prime}$ plus the paths in $\Pi$ ending with
a site $s_{i}$ that hasn’t colonized a single site. Then $\Pi^{\prime\prime}$
covers exactly the sites to be colonized, plus the start site $s_{0}$.

Since the colonization is to happen in parallel, the number of colonizing
sites, and in particular the number of available colonization probes, will
increase exponentially — or rather cubically or quadratically when physical
space constraints are taken into account (cf. section 2.2.1).141414Another way
to see the absurdity of actual exponential colonization (or reproduction) is
to note that it would lead to $\mathbf{P}\\!=\\!\mathbf{NP}$, i.e. a
polynomial time algorithm for solving e.g. the prototypical
$\mathbf{NP}$-complete problem $\mathbf{SAT}$ (boolean satisfiability): Split
the $\mathbf{SAT}$ problem in $2$ by instantiating a variable. Send out the
$2$ subproblems. Repeat until all variables are instantiated, then solve each
problem instance. Report back the result to the parent node, all the way to
the start node. If there can be an exponential colonization, then everything
takes only polynomial time. Corollary, if $\mathbf{P}\\!\neq\\!\mathbf{NP}$,
then there can’t be an infinite number of accessible dimensions.

The galaxy colonization algorithm has been implemented, and tested under
conditions intended to be Milky Way like.151515You can download the free and
open-source software here:
https://drive.google.com/file/d/1CMDNilbvGND_llkElZc2DZk7ZVi5YZM8/view The
tests indicate that the colonization plans are good. For example, the length
of the colonization path to the site colonized last is very close to the
straight line distance to that last site from the start site $s_{0}$, with the
colonization path having less than $1\%$ extra length.161616Regarding the
details of the galaxy colonization algorithm, you can first sort the sites
with regard to the distance to the start site $s_{0}$. (This can be done in
linear time with bucket sorting.) Second, the straight line distance is a
lower bound for the length of the colonization paths. Hence, when looking for
the next colonization, and if you go through the sites in order, whenever you
have a straight line distance that is greater than or equal to the shortest
colonization path found so far, you can abort that search. (So you might also
want to start searching for a site $s_{j}$ to colonize before looking for a
site $s_{i}$ to colonize from.) Since the lengths of the colonization paths
often are very close to the straight line distances, this pruning works well.

Say that the speed of the probes is $0.1\,c$ (0.1 of the speed of light).
Then, with a start site near the center and within, say, $5\times 10^{4}$
light-years of everything in the Milky Way, it would take $5\times 10^{5}$
years to colonize the whole galaxy, assuming that the colonization of a site
and the creation of new probes take relatively little time. (We can conceive
getting something weighing 2 grams up to $0.1\,c$.[236] And it ought to be
possible for a super-AI to weigh little. (2 grams corresponds to roughly 1
mole so about $10^{23}$ atoms. The adult male human brain has around 86
billion neurons.[285]) For deceleration, using stars near the targets, see
[286].)

##  Appendix C Modeling the Evolution of Cooperation and Fairness Under
Diminishing Returns

André and Baumard have modeled how fairness evolves.[2] Here we will attempt
something similar but with the added assumption of some diminishing returns.

A round in the attempted model starts off with an initial dictator or
ultimatum type game. Typically the fixed proposers are less than or equal to
the responders, which creates pressure on the proposers to offer the least
meaningful amount possible.[2] Still, the gains from this initial cycle are
assumed to be under diminishing returns.

After the initial game, a second cycle of cooperation begins. This second
cycle is some type of dictator or ultimatum game too. The top cooperator is
paired with the top lower half cooperator, with the top cooperator as
proposer, and the top lower half cooperator as responder. The second-top
cooperator is paired with the second-top lower half cooperator. And so on.

There are variations of the model: The cooperation tendencies between the two
cycles can be separate or correlated. The cooperation tendencies of the
responders may directly influence the immediate gains of the proposers in the
second cycle, to some extent. To break ties in the second cycle of
cooperation, one can treat the cooperation tendencies in the first cycle as
signals (c.f. [9, § 2.2, p. 7]) and favor entities with higher first cycle
offers, when applicable.

The model and its variations have been implemented.171717The free and open-
source software is available at:
https://drive.google.com/file/d/1oiUynLyCHf9dYzPr6cj5aSDzj_iJ0Ef1/view Each
round takes linear time in the size of the population.

Simulation runs suggest, as is to be expected, that the first cycle of
cooperation is of secondary importance. For example, offers less than half of
the pie will be roughly equally good when it comes to direct, immediate gains.
Hence, if there are other aspects to the offer, e.g. “signaling” or that gains
in the second cycle of cooperation are affected, those other aspects will come
into play; offers will be larger under diminishing returns than, say, linear
returns.

Simulation runs suggest too that cooperation and fairness evolve. In
particular, if the responders contribute to the gain of the proposers in the
second cycle of cooperation, then the proposers eventually offer more than
half the pie, because of pressure to be paired with good responders. If the
offer in the first cycle can’t be much lower than the second cycle offer, then
the first cycle offer too will eventually be more than half the pie. If there
is no extra contribution from the responders in the second cycle, then the
offers will settle around half the pie.

##  Appendix D On How to Eat

### D.1 Vegetarianism and Veganism

Humans might be moving towards something like vegetarianism or veganism,
ostensibly signaling considerateness and cooperativeness (cf. [88, 287, 288,
289, 290, 291, 292, 293, 294, 295, 59]). For example, and to emphasize the
generational aspect of the progression, in Sweden 20% of young women said that
they are vegetarians or vegans, while about 1% of the men at age 50 or above
said the same.[296] (The reference also illustrates that women tend to be more
progressive than men, in certain areas.[297, 96, 91] Of the young men, 10 %
said that they are vegetarians or vegans, and 3.5 % of women 50 or above said
the same (cf. [293, 291]). Women (and men) preferring considerate mates is a
further aspect and driver.[159, e.g. table 5][33]) See also [298, 299, 300,
301] for more comprehensive reviews of the prevalence of vegetarianism and
veganism.

If humans are getting more and more considerate, then maybe killing animals to
eat them will increasingly be considered wrong. Even more so because not
eating meat is typically likely healthier,[302, 303, 304, 305, 306, 293] meat
is an order of magnitude more inefficient and expensive,[307, 308] and the
typical meat production harms the environment [303, 291, 305, 309, 310, 306,
302, 293].181818↑ Here are examples of vegan diets:  1. A frugal and easy
example, stew and porridge, nutrient-sufficient, 1785 kcal (the calories
Okinawans ate [171]):
https://drive.google.com/file/d/1Sfhfpvu3TsD4dMQHEkuCzJ_S_w2dfrUb/view 2. A
more theoretical version of diet 1, 1240 kcal:
https://drive.google.com/file/d/1mCkbgabokQTAyvFBLQo2UHRMVJbdJ4TY/view 3.
Spinach, 8 kg, 1840 kcal, which, nominally at least, gives you all prescribed
micro and macro nutrients, in excess, apart from vitamins B12 and D:
https://drive.google.com/file/d/1uXENw74pi_FRtpwQvypZXCmhq5OrZ-tw/view 4. A
fatty and sugary cake mix, 1 kg, 6300 kcal:
https://drive.google.com/file/d/1zv_5Pip2KVXq-8nuSGHpWSBv3u4t86iv/view
Ostensibly, humans have a taste for fat and sugar (and salt) in order to get
enough energy (and salt), in a bygone environment that didn’t have a limitless
supply of fatty and sugary (and salty) products readily available;[311] with
an environment short of fatty and sugary items, you might typically get an
insufficient amount of energy (cf. diets 2 and 3); with an environment flooded
with fatty or sugary items, you might typically get too much energy (cf. diet
4); and with something in between you should typically get an adequate amount
of energy (cf. diet 1); see section 1.4.2.

Note too that the above discussion suggests that honest signals of veganism,
and to a lesser extent vegetarianism, are examples of signals with negative
cost. Cf. [47, 48]191919↑ Here is a free and open-source program that
simulates the development of signaling, following and generalizing [48]:
https://drive.google.com/file/d/11baCngUn-gWwSbMa6s8jJ1G_9MoZLiSP/view. (That
the cost is of the essence for signals is a misunderstanding; if a signal
happens to have negative cost, all the better.[47, 48] Other similar acts
signaling cooperativeness, and with negative cost, might include: running or
cycling instead of driving; not smoking; forgoing manicured gardens and lawns;
and perhaps bows instead of handshakes, and non-consumerism.)

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