Seasonality and competition select for variable germination behavior in perennials

The common occurrence of within-population variation in germination behavior and associated traits such as seed size has long fascinated evolutionary ecologists. In annuals, unpredictable environments are known to select for bet-hedging strategies causing variation in dormancy duration and germination strategies. Variation in germination timing and associated traits is also commonly observed in perennials, and often tracks gradients of environmental predictability. Although bet-hedging is thought to occur less frequently in long-lived organisms, these observations suggest a role of bet-hedging strategies in perennials occupying unpredictable environments. We use complementary numerical and evolutionary simulation models of within- and among-individual variation in germination behavior in seasonal environments to show how bet-hedging interacts with density dependence, life-history traits, and priority effects due to competitive differences among germination strategies. We reveal substantial scope for bet-hedging to produce variation in germination behavior in long-lived plants, when "false starts" to the growing season results in either competitive advantages or increased mortality risk for alternative germination strategies. Additionally, we find that two distinct germination strategies can evolve and coexist through negative frequency-dependent selection. These models extend insights from bet-hedging theory to perennials and explore how competitive communities may be affected by ongoing changes in climate and seasonality patterns.

Improved Characterization of Balancing Selection Genome Wide and a Detailed Look at HLA Genes

Balancing selection occurs when different evolutionary pressures impact the fitness of multiple alleles, resulting in increased allelic diversity in the population. A new statistical method was developed to test for selection, improving inference by using efficient Bayesian techniques to test for density and strength of linkage disequilibrium. Evolutionary simulation studies showed that the method consistently outperformed existing methods. Using this methodology, we tested for novel signals of balancing selection genome wide in 500 samples from phased trios. Several novel signals of selection appeared in CYP2A7, GPC6, and CNR2 across multiple ancestries. Additionally, tests in SIRPA demonstrate dramatically strong selection signal, significantly higher than previously observed. Well-known signals around olfactory genes and the MHC, containing HLA genes associated with the immune response, also demonstrated strong signatures of selection. So, utilizing data from the 17th IHIW, a follow up analysis was then performed by leveraging over seven thousand HLA typed samples by NGS; in contrast, the genome wide scan did not include a detailed characterization of the HLA genes. The strongest signals observed in the IHIW samples were in DQA1 and DQB1 in or around exon 2, the portion of the gene responsible for antigen presentation and most likely to be under environmental and evolutionary pressure. Our new statistical approach and analysis suggest novel evolutionary pressure in new regions and additionally highlight the importance of improved sequencing and characterization of variation across the extended MHC and other critical regions.

Metric Routing Protocol for Detecting Untrustworthy Nodes for Packet Transmission

IoT objects that have a resource constrained nature resulting in a number of attacks in the routing protocol for lossy networks and low-power networks. RPL is very vulnerable to selfish behaviours and internal attacks though they are built with encryption protection to secure messages. To address this vulnerability, in this paper, we propose a novel trustworthiness methodology based on metric for incorporating trust evaluation, enhancing the robustness of security mechanism. Simulation results indicate that the proposed work is efficient in terms of throughput, nodes’ rank changes, energy consumption and packet delivery ratio. Moreover, using mathematical modelling, it has been observed that this methodology meets the demands of loop-freeness, optimality and consistency. This shows that this metic has both monotonicity and isotonicity requirements to enable the routing protocol. Incorporating the concepts of game theory, we can use this technique as a strategy to iterate Prisoner’s Dilemma. Both evolutionary simulation and mathematical analysis indicate that the proposed metric-based routing protocol is an efficient technique in promoting evolution and stability of the IoT network.

Learning, exploitation and bias in games

We focus on learning during development in a group of individuals that play a competitive game with each other. The game has two actions and there is negative frequency dependence. We define the distribution of actions by group members to be an equilibrium configuration if no individual can improve its payoff by unilaterally changing its action. We show that at this equilibrium, one action is preferred in the sense that those taking the preferred action have a higher payoff than those taking the other, more prosocial, action. We explore the consequences of a simple ‘unbiased’ reinforcement learning rule during development, showing that groups reach an approximate equilibrium distribution, so that some achieve a higher payoff than others. Because there is learning, an individual’s behaviour can influence the future behaviour of others. We show that, as a consequence, there is the potential for an individual to exploit others by influencing them to be the ones to take the non-preferred action. Using an evolutionary simulation, we show that population members can avoid being exploited by over-valuing rewards obtained from the preferred option during learning, an example of a bias that is ‘rational’.

Friendly-rivalry solution to the iterated n-person public-goods game

Repeated interaction promotes cooperation among rational individuals under the shadow of future, but it is hard to maintain cooperation when a large number of error-prone individuals are involved. One way to construct a cooperative Nash equilibrium is to find a ‘friendly-rivalry’ strategy, which aims at full cooperation but never allows the co-players to be better off. Recently it has been shown that for the iterated Prisoner’s Dilemma in the presence of error, a friendly rival can be designed with the following five rules: Cooperate if everyone did, accept punishment for your own mistake, punish defection, recover cooperation if you find a chance, and defect in all the other circumstances. In this work, we construct such a friendly-rivalry strategy for the iterated n-person public-goods game by generalizing those five rules. The resulting strategy makes a decision with referring to the previous m = 2n − 1 rounds. A friendly-rivalry strategy for n = 2 inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of a neutral mutant. Our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions when n = 2 and 3.

Five rules for friendly rivalry in direct reciprocity

Direct reciprocity is one of the key mechanisms accounting for cooperation in our social life. According to recent understanding, most of classical strategies for direct reciprocity fall into one of two classes, ‘partners’ or ‘rivals’. A ‘partner’ is a generous strategy achieving mutual cooperation, and a ‘rival’ never lets the co-player become better off. They have different working conditions: For example, partners show good performance in a large population, whereas rivals do in head-to-head matches. By means of exhaustive enumeration, we demonstrate the existence of strategies that act as both partners and rivals. Among them, we focus on a human-interpretable strategy, named ‘CAPRI’ after its five characteristic ingredients, i.e., cooperate, accept, punish, recover, and defect otherwise. Our evolutionary simulation shows excellent performance of CAPRI in a broad range of environmental conditions.

Consciousness Fluctuates with Surprise: An empirical pre-study for the synthesis of the Free Energy Principle and Integrated Information Theory

The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical frameworks, the first aiming to make a general formal description of self-organization and life-like processes, and the second attempting a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior the other with meaning and experience, so combining them has potentially great scientific value. In this paper, we take a first step towards this synthesis by first partially replicating the results of the evolutionary simulation study by Albantakis et al. (2014) that show a relationship between IIT-measures and fitness in differing complexities of tasks. We then relate FEP-related information theoretic measures to this result, finding that the surprisal of simulated agents’ system states follows the general increase in fitness over evolutionary time, and that it fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures of IIT indirectly depend on the relation between the agent and the external world, and that they therefore should be related to concepts directly used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically.

Friendly-rivalry solution to the iterated n-person public-goods game

Repeated interaction promotes cooperation among rational individuals under the shadow of future, but it is hard to maintain cooperation when a large number of error-prone individuals are involved. One way to construct a cooperative Nash equilibrium is to find a ‘friendly rivalry’ strategy, which aims at full cooperation but never allows the co-players to be better off. Recently it has been shown that for the iterated Prisoner’s Dilemma in the presence of error, a friendly rival can be designed with the following five rules: Cooperate if everyone did, accept punishment for your own mistake, punish defection, recover cooperation if you find a chance, and defect in all the other circumstances. In this work, we construct such a friendly-rivalry strategy for the iterated n-person public-goods game by generalizing those five rules. The resulting strategy makes a decision with referring to the previous m = 2n − 1 rounds. A friendly-rivalry strategy inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions.Author summaryHow to maintain cooperation among a number of self-interested individuals is a difficult problem, especially if they can sometimes commit error. In this work, we propose a strategy for the iterated n-person public-goods game based on the following five rules: Cooperate if everyone did, accept punishment for your own mistake, punish others’ defection, recover cooperation if you find a chance, and defect in all the other circumstances. These rules are not far from actual human behavior, and the resulting strategy guarantees three advantages: First, if everyone uses it, full cooperation is recovered even if error occurs with small probability. Second, the player of this strategy always never obtains a lower long-term payoff than any of the co-players. Third, if the co-players are unconditional cooperators, it obtains a strictly higher long-term payoff than theirs. Therefore, if everyone uses this strategy, no one has a reason to change it. Furthermore, our simulation shows that this strategy will become highly abundant over long time scales due to its robustness against the invasion of other strategies. In this sense, the repeated social dilemma is solved for an arbitrary number of players.

Experimental evolutionary simulations of learning, memory and life history

Humans possess an unusual combination of traits, including our cognition, life history, demographics and geographical distribution. Many theories propose that these traits have coevolved. Such hypotheses have been explored both theoretically and empirically, with experiments examining whether human behaviour meets theoretical expectations. However, theory must make assumptions about the human mind, creating a potentially problematic gap between models and reality. Here, we employ a series of ‘experimental evolutionary simulations' to reduce this gap and to explore the coevolution of learning, memory and childhood. The approach combines aspects of theory and experiment by inserting human participants as agents within an evolutionary simulation. Across experiments, we find that human behaviour supports the coevolution of learning, memory and childhood, but that this is dampened by rapid environmental change. We conclude by discussing both the implications of these findings for theories of human evolution and the utility of experimental evolutionary simulations more generally. This article is part of the theme issue ‘Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals'.