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| − | On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by [[Peter Turchin]] and Mikhail Burtsev in 2006. Previously, [[game theory]] has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon [[Conway's Game of Life]] but with much added complexity (there are over 10<sup>1000</sup> strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-[[hawk-dove game]], here two novel modes of cooperative attack and defense arise from the simulation.
| + | 在生态方面，2006年，[[彼得·图尔钦]]（Peter Turchin）和[[米哈伊尔·伯切夫]]（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的[[汉密尔顿]]（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论<ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,1390–1396 (1981).</ref>)。在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过10<sup>1000</sup>种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-[[鹰派-鸽派对策策略]]之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。 |
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| − | 在生态方面，2006年，[[彼得·图尔钦]]（Peter Turchin）和[[米哈伊尔·伯切夫]]（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过10<sup>1000</sup>种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。
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| | 对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。 | | 对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。 |
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| − | The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.
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| − | The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.
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| | 本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。 | | 本研究旨在探讨表型标记的存在对模型合作策略演化范围的影响。此外，由于该二维环境的可用资源是有限的，模拟还可以确定环境承载力对其涌现的影响。 |
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| − | One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the [[mobbing behavior]] that characterizes many species of small birds when they collectively defend against the predator.
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| − | One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.
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| | 一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。 | | 一种前所未见的策略被称为“乌鸦”。这些因子使细胞与群内成员共存，从而避免了群内竞争，并主动攻击群外成员。另一种名为“starling”的策略是让因子与组内成员共享细胞。尽管由于资源分割，个体拥有较小的能量存储，但这种策略可以通过数量上的优势，对大型入侵者进行高效防御。从生态学的角度来说，这类似于许多小型鸟类在集体防御捕食者时所具有的聚众滋扰行为。 |
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| | + | 总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>。 |
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| − | | + | 创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即[[蓝色基因]]项目，该项目旨在了解[[蛋白质折叠]]背后的机制。 |
| − | In conclusion, the research claims that the simulated results have important implications for the evolution of [[territoriality (ethology)|territoriality]] by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".<ref>Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature</ref>
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| − | In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".
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| − | 总之，研究认为，模拟结果表明，在人工生命框架内，“不仅可以模拟一种战略如何取代另一种战略，而且可以模拟新战略从大量可能性中产生的过程”，从而对地域性的演变具有重要意义。
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| − | Work is also underway to create [[computational biomodeling|cellular models of artificial life]]. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely [[Blue Gene]] which seeks to understand the mechanisms behind [[protein folding]].
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| − | Work is also underway to create cellular models of artificial life. Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.
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| − | 创造人工生命细胞模型的工作也在进行中。作为许多不同研究项目的一部分，建立细胞行为的完整生化模型的初步工作正在进行中，即蓝色基因项目，该项目旨在了解蛋白质折叠背后的机制。
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| | == See also == | | == See also == |