Evolution of cooperation without iterations and without relatedness: strategic behaviour in public goods games and 2-person games

无迭代、无关联的合作演化：公共物品博弈和两人博弈中的策略行为

• 批准号: NE/H015701/1
• 负责人: Marco Archetti
• 金额: $ 36.86万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH015701%2F1
• 关键词: Evolution; cooperation; without; iterations; relatedness

Many interactions in nature are antagonistic because Darwinian natural selection leads to the survival of the fittest, and one's advantage is usually someone else's disadvantage. Yet, many cases of altruism and cooperation exist: from sterile soldiers in ants that do not reproduce and only work for the colony, to sentinels in meerkats that give the alarm in case of predators approaching. Altruists pay a cost for helping other individuals, a cost that selfish individuals do not pay. Because altruist and selfish individuals ultimately compete for reproduction, the selfish individuals should have an advantage. How can we explain the existence of cooperation then? One solution is that altruists are usually family members: ants and other social insects for example help their sisters by helping the nest. In this way they favour the spread of their own genes, because the targets of their altruism bear the same genes with high probability. Another solution in that altruism can be directed towards individuals that are in a position to reciprocate in successive encounters. Being altruist, therefore, may pay back because altruists receive a benefit from those whom they have helped. These two explanations, however, are not fully satisfactory, because there are many cases of symbiosis and cooperation in which individuals are not related and will never meet again, and yet cooperation exists in these cases. My work aims at understanding how these cases are possible. I do this by developing models of game theory, the branch of mathematics developed from the work of John Nash (the 'Beautiful Mind' of the famous movie). A game, in mathematics, is the description of a situation in which two players are in conflict and each tries to get the maximum payoff. Mathematics is necessary because the results are not always intuitive. Consider for example the case of a group of people witnessing a crime. If one of them called the police the criminal could be arrested. Arresting the criminal is a public good. Calling the police, however, has a small cost for an individual, and when there are many witnesses everybody prefers that it is someone else that calls the police. Everybody is better off if the criminal is arrested, but everybody prefers that it is somebody else that takes the risk and pays the cost. One would think that, when more people are available to volunteer, the probability that someone calls the police increases; in fact when too many people witness a crime, usually nobody volunteers to help. This is the effect of strategic behaviour - everybody relies on someone else with a certain probability, and as the number of possible volunteers increases, this probability increases, and it increases more when the number of witnesses is larger. In fact when one is the only (or one of few) possible volunteer, he is usually more likely to help. This is not intuitive, but many example have been documented, and it can be demonstrated by game theory. Mathematics is useful also because it can suggest precise and practical predictions. In the case of cooperation these predictions can help us devise strategies to increase cooperation among selfish individuals. For example, how is it possible to induce people to call the police more often? One solution is to reduce (not to increase!) the ability of a part of the witnesses to call the police, for example by impairing their ability to make a phone call, and to make this evident to everybody. When only a few witnesses can actually help, these ones will be more willing to volunteer. In my work I analyse similar, more complicated cases in which individual actions, that can be selfish, can provide a collective good, and I suggest strategic solutions to increase cooperation in these situations.

自然界中的许多相互作用都是对抗性的，因为达尔文的自然选择导致适者生存，一个人的优势通常是另一个人的劣势。然而，利他主义和合作的例子还是存在的：从蚂蚁中的不育士兵，他们不繁殖，只为殖民地工作，到猫鼬中的哨兵，他们在捕食者接近时发出警报。利他主义者为帮助其他人付出代价，而自私的人则不会付出代价。因为利他主义者和自私的个体最终会为繁殖而竞争，所以自私的个体应该有优势。那么，我们如何解释合作的存在呢？一个解决办法是利他主义者通常是家庭成员：例如蚂蚁和其他社会性昆虫通过帮助巢穴来帮助它们的姐妹篇。通过这种方式，他们有利于自己基因的传播，因为他们利他主义的目标极有可能携带相同的基因。另一个解决方案是，利他主义可以针对那些在连续遭遇中处于互惠状态的个人。因此，利他主义者可能会得到回报，因为利他主义者从他们帮助过的人那里得到了好处。然而，这两种解释并不完全令人满意，因为在许多共生和合作的情况下，个体没有关系，永远不会再见面，但在这些情况下存在合作。我的工作旨在了解这些情况是如何可能的。我通过发展博弈论的模型来做到这一点，博弈论是数学的分支，是从约翰·纳什（著名电影中的“美丽心灵”）的工作发展起来的。在数学中，游戏是对两个参与者处于冲突中并且每个人都试图获得最大收益的情况的描述。数学是必要的，因为结果并不总是直观的。例如，考虑一群人目睹犯罪的情况。如果其中一个人报警，罪犯就可能被逮捕。逮捕罪犯是一项公益事业。然而，报警对个人来说成本很小，当有很多目击者时，每个人都希望是别人报警。如果罪犯被逮捕，每个人都更好，但每个人都希望由其他人承担风险并支付成本。有人会认为，当有更多的人可以自愿时，有人报警的可能性就会增加;事实上，当太多的人目睹犯罪时，通常没有人自愿帮助。这是策略行为的结果--每个人都有一定的概率依赖于其他人，随着可能的志愿者数量的增加，这种概率也会增加，当目击者的数量增加时，这种概率会增加得更多。事实上，当一个人是唯一（或少数几个）可能的志愿者时，他通常更有可能提供帮助。这不是直观的，但有许多例子已经记录在案，它可以通过博弈论来证明。数学也是有用的，因为它可以提出精确和实用的预测。在合作的情况下，这些预测可以帮助我们设计策略，以增加自私个体之间的合作。例如，如何才能促使人们更经常地报警？一个解决方案是减少（而不是增加！）一部分证人打电话报警的能力，例如通过削弱他们打电话的能力，并使每个人都能看到这一点。当只有少数目击者能真正提供帮助时，这些人会更愿意自愿。在我的工作中，我分析了类似的，更复杂的情况下，个人的行动，可以是自私的，可以提供一个集体的利益，我建议战略解决方案，以增加在这些情况下的合作。

项目成果

Evolutionarily stable anti-cancer therapies by autologous cell defection.

• DOI: 10.1093/emph/eot014
• 发表时间: 2013-01
• 期刊: Evolution, medicine, and public health
• 作者: Archetti M

Evolutionary game theory of growth factor production: implications for tumour heterogeneity and resistance to therapies.

• DOI: 10.1038/bjc.2013.336
• 发表时间: 2013-08-20
• 期刊: British journal of cancer
• 影响因子: 8.8
• 作者: Archetti M

Dynamics of growth factor production in monolayers of cancer cells and evolution of resistance to anticancer therapies.

• DOI: 10.1111/eva.12092
• 发表时间: 2013-12
• 期刊: Evolutionary applications
• 影响因子: 4.1
• 作者: Archetti M

Heterogeneity and proliferation of invasive cancer subclones in game theory models of the Warburg effect.

• DOI: 10.1111/cpr.12169
• 发表时间: 2015-04
• 期刊: Cell proliferation
• 影响因子: 8.5
• 作者: Archetti M

Stable heterogeneity for the production of diffusible factors in cell populations.

• DOI: 10.1371/journal.pone.0108526
• 发表时间: 2014
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Archetti M