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A Theoretical Treatment of Memetic Traits Using Gene-Meme, Meme-Meme and Population Equilibrium
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Background: Conceived as a unit of lasting cultural (mostly vertical) trait transmission, memes now include transient horizontally transmitted fads. Memes may sometimes follow the logic of population genetics, e.g. learned birdsong, but not always over the diverse range found in human hosts. Much current work focuses on selection of memes rather than their hosts. Methods: We analyze equilibrium between gene-meme and meme-meme competing propagators and consider whether a meme is linked to reproduction (e.g. vertical culture transmission), or not. We employ a genetic component and combined meme induced fitness components for hosts, while memes have replication factors to distinguish from what's good for the host (fitness). To anticipate future meme effects on population stability we use a Monte Carlo simulation roughly calibrated to the Industrial Revolution. Results: A basic effective calculus of memetic trait competition and interaction with genes is derived and analyzed. The transient nature of short term memes may be a defense against accumulation of deleterious memes. Horizontally transmitted (panmitic) memes with high spreading rate will often not equalize with a genetic trait, spreading outside of natural selection of the hosts, presenting a cumulative existential threat. Vertical transmission reduces rep-lication rate and allows group selection against deleterious memes. Conclusions: The advantage of a portfolio of groups or species may not accrue to a single group. This analytical understanding elevates meme-risk to the level of a candidate solution to the so-called Fermi Paradox, as interstellar travel might require a planet wide group.
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... For those curious, the author has analyzed the risk of shared human behavior traits which are not subject to biological selection elsewhere. [22] References [ [13] Handel, A., Miller, J.C., Ge, Y., Fung, I.C. (2020). If containment is not possible, how do we minimize mortality for COVID-19 and other emerging infectious disease outbreaks? ...
Background: Late phase COVID-19 strategic decisions are being made for vaccine protocols such as selective vs. fast. Quantitative trade studies have not appeared. A new faster spreading strain has developed. Quantitative analysis of how it will affect vaccination schedules and final unlock protocol are needed. Lessons learned papers so far deal with prevention of spreading and practice of care issues, leaving major strategy questions about vaccination approach and final unlock open. Methods: We use an SIR-based model tuned for COVID-19 and accounting for seasonality, immunity decline, and vaccination. We add the capability to handle a changing fundamental replication rate (R0) and declining ratio of total to known cases (determined by data fitting). Results: For the present R0 and vaccination schedule a full internal U.S. unlock in April or May 2021 is supportable, however infections reintroduced would propagate in the fall if immunity wears off in 240 days. If the increased R0 becomes established then the infection is never completely extinguished and recurs. If immunity lasts 240 days, re-vaccination is not needed until 2022 even with increased R0 making doses available for use in other countries. Likely 77,000 lives were lost by not meeting the initially planned vaccination targets. Conclusions: The usefulness of trade studies in saving lives is established. Intuitively appealing strategies are not necessarily optimal. Lack of inexpensive random sample testing hampers ability to do trade studies. Phase 3 vaccine trial protocols for epidemics with heavy loss of life should be revisited.
This paper investigates cooperation games in which poor agents do not benefit from cooperation with wealthy agents. They instead benefit from considering wealth relative to decision payoffs of fitness or wealth. Of concern is the effect of cooperation on participants, their rational self-interest and choices, and not the evolution of cooperation directly. The accumulation of fitness or wealth has been shown in the literature to lead to different optimal strategies for wealthy and poor players in Chicken games. The effect could have important explanatory power if it were more broadly applicable. First we empirically compare two published results, one involving the temptation parameter vs. degree of cooperation in Prisoner's Dilemma, and the other a surprising result from a public goods game with participants from different cultures, networks and wealth in which a fixed rather than relative payoff scheme was used. Using the temptation data to calibrate the public goods behavior suggests wealth factors can provide an explanation for the results. Second we show using simulation that adding a survival threshold to a wealth or fitness accumulating Iterated Prisoner's Dilemma produces a wealth relative effect. We clarify previous results to show the poor must avoid survival risk, regardless of whether this is associated with cooperation or defection. We do this by introducing the Farmer's Game, a simulation of Iterated Prisoner's Dilemma with wealth accumulation and a survival threshold. This is used to evaluate the Tit-for-Tat strategy and four variants. Equilibrium payoffs keep the game scaled to social relevance, with a fraction of all payoffs externalized as a turn cost parameter. Findings include poor performance of Tit-for-Tat near the survival threshold, superior performance of low risk strategies for both poor and wealthy players, dependence of survival of the poor near the threshold on Tit-for-Tat forgiveness, unexpected optimization of forgiveness without encountering a social dilemma, improved performance of a diverse mix of strategies, and a more abrupt threshold of social catastrophe for the better performing mix. Lastly we compare cooperating and non-cooperating societies using the simulation and discover disturbing connections between cooperation and familiar non-egalitarian wealth distribution patterns.
The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain, mediates various forms of synaptic plasticity, and has been implicated in neurodevelopmental disorders. However, little is known about Arc's molecular function and evolutionary origins. Here, we show that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells, where it can undergo activity-dependent translation. Purified Arc capsids are endocytosed and are able to transfer Arc mRNA into the cytoplasm of neurons. These results show that Arc exhibits similar molecular properties to retroviral Gag proteins. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestors to retroviruses. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system.
Social influence can lead to behavioural ‘fads’ that are briefly popular and quickly die out. Various models have been proposed for these phenomena, but empirical evidence of their accuracy as real-world predictive tools has so far been absent. Here we find that a ‘complex contagion’ model accurately describes the spread of behaviours driven by online sharing. We found that standard, ‘simple’, contagion often fails to capture both the rapid spread and the long tails of popularity seen in real fads, where our complex contagion model succeeds. Complex contagion also has predictive power: it successfully predicted the peak time and duration of the ALS Icebucket Challenge. The fast spread and longer duration of fads driven by complex contagion has important implications for activities such as publicity campaigns and charity drives.
Humans have a sophisticated ability to learn from others, termed social learning, which has allowed us to spread over the planet, construct complex societies, and travel to the moon. It has been hypothesized that social learning has played a pivotal role in making human societies cooperative, by favouring cooperation even when it is not favoured by genetical selection. However, this hypothesis lacks direct experimental testing, and the opposite prediction has also been made, that social learning disfavours cooperation. We experimentally tested how different aspects of social learning affect the level of cooperation in public-goods games. We found that: (i) social information never increased cooperation and usually led to decreased cooperation; (ii) cooperation was lowest when individuals could observe how successful individuals behaved; and (iii) cooperation declined because individuals preferred to copy successful individuals, who cooperated less, rather than copy common behaviours. Overall, these results suggest that individuals use social information to try and improve their own success, and that this can lead to lower levels of cooperation. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
This article explores how much memes like urban legends succeed on the basis of informational selection (i.e., truth or a moral lesson) and emotional selection (i.e., the ability to evoke emotions like anger, fear, or disgust). The article focuses on disgust because its elicitors have been precisely described. In Study 1, with controls for informational factors like truth, people were more willing to pass along stories that elicited stronger disgust. Study 2 randomly sampled legends and created versions that varied in disgust; people preferred to pass along versions that produced the highest level of disgust. Study 3 coded legends for specific story motifs that produce disgust (e.g., ingestion of a contaminated substance) and found that legends that contained more disgust motifs were distributed more widely on urban legend Web sites. The conclusion discusses implications of emotional selection for the social marketplace of ideas.
Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grid topologies. We show that the increased takeover time induced by the latter is essential for improving the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates. Experiments realized with an actual MMA on three different complex pseudo-Boolean functions are consistent with these findings, indicating that memes are more successful in a spatially structured MMA, rather than in a panmictic MMA, and that the performance of the former is significantly better than that of its panmictic counterpart
We study the origin of the log-normal popularity distribution of trending memes observed in many real social networks. Based on a biological analogy, we introduce a fitness of each meme, which is a natural assumption based on sociological reasons. From numerical simulations, we find that the relative popularity distribution of the trending memes becomes a log-normal distribution when the fitness of the meme increases exponentially. On the other hand, if the fitness grows slowly, then the distribution significantly deviates from the log-normal distribution. This indicates that the fast growth of fitness is the necessary condition for the trending meme. Furthermore, we also show that the popularity of the trending topic grows linearly. These results provide a clue to understand long-lasting questions, such as what causes some memes to become extremely popular and how such memes are exposed to the public much longer than others.
This article reports the findings from simulating the spatial diffusion processes of memes over social media networks by using the approach of agent-based modeling. Different from other studies, this article examines how space and distance affect the diffusion of memes. Simulations were carried out to emulate and to allow assessment of the different levels of efficiency that memes spread spatially and temporally. Analyzed network structures include random networks and preferential attachment networks. Simulated spatial processes for meme diffusion include independent cascade models and linear threshold models. Both simulated and real-world social networks were used in the analysis. Findings indicate that the numbers of information sources and opinion leaders affect the processes of meme diffusion. In addition, geography is still important in the processes of spatial diffusion of memes over social media networks.
