Natural Reward Podcast Owen Gilbert

Ulrike Bauer discusses the evolution and diversity of pitcher plants, focusing on the spring trapping mechanism found in some species. Pitcher plants are carnivorous plants that capture insects in a fluid-filled cavity. They have evolved independently multiple times and are found all over the world. The spring trapping mechanism is a composite trait that involves multiple adaptations, including a horizontal lid, a spring-like structure, and a slippery surface. The study of this mechanism involved fieldwork, experiments, and collaboration between researchers with expertise in ecology, biomechanics, and evolutionary biology. In this part of the conversation, Ulrike discusses the evolution of a composite trait and the opportunity to study how such a trait can evolve independently in different species. She explains how she came up with hypotheses and tested them to understand the evolution of the spring trapping plant. The conversation also explores the absence of transitional stages in the fossil record and the role of randomness in the emergence of complex traits. Ulrike's research challenges the traditional narrative of goal-directed evolution and highlights the importance of considering alternative mechanisms. The conversation explores the evolution of complex traits and the emergence of their functions. It discusses the stepwise process of trait evolution, such as self-incompatibility in plants and the evolution of pitcher plants. The incidental effects of complex traits on extinction rates and the maintenance of sexual reproduction are also examined. The concept of innovation in evolutionary biology is explored, highlighting the importance of variation and the role of selection in generating novelty. The challenges of studying complex trait evolution and the need for more empirical studies are discussed.

Takeaways


Pitcher plants are carnivorous plants that have evolved independently multiple times and are found all over the world.The spring trapping mechanism in pitcher plants is a composite trait that involves multiple adaptations.The spring trapping mechanism is an example of a moving trap that employs movement to capture prey.The study of the evolution of pitcher plants involved fieldwork, experiments, and collaboration between researchers with different areas of expertise. Composite traits can evolve independently in different species, providing an opportunity to study the evolution of complex traits.Hypotheses can be formulated and tested to understand the mechanisms behind the evolution of composite traits.The absence of transitional stages in the fossil record challenges the traditional narrative of goal-directed evolution.Randomness and variability play a significant role in the emergence of complex traits. Complex traits often evolve through a stepwise process, gradually building upon existing traits to create new functions.Incidental effects of complex traits can have significant ecological and evolutionary consequences, such as influencing extinction rates.The distinction between invention and innovation is important in understanding the origin and spread of complex traits.Variation is a key factor in generating novelty and driving the evolution of complex traits.Studying the origin of complex traits can provide valuable insights into the mechanisms of evolution.

Illegal dumping is a widespread problem in cities throughout the world and differentially affects disadvantaged neighborhoods.  Brian Johnson is a software engineer who moved to San Francisco nearly a decade ago. At the time, Brian could afford a house only in the least-expensive neighborhood, Bayview. Despite hopes for improvement, over time Bayview declined because of an illegal dumping problem.  To protect his children, Brian started brainstorming ways to solve this problem. The problem is difficult because dumping laws are difficult to enforce and people can easily get away with the crime.  Brian's solution was to automate drones to fly in grid-like patterns, take photos of a neighborhood, instantly recognize trash heaps using artificial intelligence (AI), and automatically report the locations of the trash piles to 311.  Brian tested many different types of AI and programmed the drones to automatically report trash heaps. This resulted in major improvements in his neighborhood, recognized by neighbors and by Brian's own tests. However, Brian is still seeking to scale up his project to help other neighborhoods and cities and seeks funding for the project.  Brian, who has a law degree and specialized in intellectual property, also wrote a patent for his system, not to prevent other people from doing this, but to prevent other people from preventing him from doing it.  Brian's solution leads to more unbiased was of reporting trash piles that can yield more equitable outcomes. Otherwise, city trash collectors may be called to affluent neighborhoods more often. Brian shows a number of photos taken by his drone in the video and explains how he trains the artificial intelligence to recognize trash heaps.  Brian has applied for an NSF grant and to join Y Combinator.

In this episode, Jon and I discuss some of the background to the previous episode. We discuss generalized versions of Hamilton's rule, Fisher's fundamental theorem, and Wright's fitness maximization formula. W. D. Hamilton used Sewall Wright's formula as the foundation of the theory of inclusive fitness. We discuss Wright's shifting balance theory and the role that Wright's formula played in his theory. We also discuss the difference between Wright's rendition of Fisher's fundamental  theorem and Fisher's formula. We compare the progress of social theory to a telephone game. Finally, Jon explains why we might need the equivalent of "poll requests" when it comes to debugging the software of social evolution.

In this episode, my brother Jon and I discuss my work on the evolution of kin recognition. Jon is a software engineer and likes to put my arguments in terms of debugging software. For many years, the mere finding of kin recognition in nature was taken as prima facie evidence of W. D. Hamilton's theory of "inclusive fitness." A large paradigm was built on the teleological assumption that kin recognition is evidence of the final cause of "inclusive fitness maximization."  A major anomaly to this paradigm called "Crozier's paradox," analogous to a software bug, suggested that kin recognition could not evolve for directing altruism to kin.  When I finally resolved Crozier's paradox almost 30 years after it first appeared, the implications were extremely disruptive. As Jon would put it, much of the "software" of social evolution came to depend on the assumptions that led to Crozier's paradox. By questioning these assumptions, my theory implied that social evolutionists had misunderstood the adaptive basis of kin recognition, incorrectly tested Hamilton's rule, and misinterpreted Darwinism. Particularly, social evolutionists had misinterpreted Darwin's theory as teleological and tried to justify this teleology with generalized mathematical equations, like inclusive fitness or generalized versions of Hamilton's rule. Jon and I discuss how that theorists rejected my work because it did not conform to their prior expectations about what "general theory" is supposed to be, even though it yielded novel predictions for the genetics and evolution of kin recognition that were upheld by 50 years of evidence. We end this podcast with a brief discussion of the differences between scientific peer review and open software forums that allows "bugs" to persist in science. This episode is essential listening for anyone who wants to know what is wrong with science today. References and notes for this episode can be found at the natural reward blog.

This is my first conversation with Chris Fortier, Vice President of the Web 3.0 company Rally. It begins when Chris questions me on what he calls my "natural forces" theory, which invokes two nonrandom forces of evolution: natural selection and natural reward. Relevant to this theory, we discuss the concept of teleology, especially as it relates to evolutionary adaptation and experimental evolution in microorganisms. We then review the "major transition” framework for classifying evolutionary innovation in terms of levels of organization, cooperation, and information storage and transmission, and how they relate to the Web 1/2/3 scheme. We then forge analogies between the cooperative and informational problems facing planetary life and humans operating in the technoworld, with reference to game theoretical dilemmas. In the latter half of the conversation, I tell Chris about my project investigating the economics of science and how to make science more innovative by altering its funding structure. Chris immediately grasps my approach to the problem and speculates about a solution. We then discuss what Chris is doing at Rally and I query him about its economics and governance. Finally, I tell Chris about my proposed solution and Chris is ready with examples from the cryptoworld that support my approach. At the end, Chris answers my question of how Rally itself stands to profit along with the creators within it. This conversation is an explosion of dynamism, where a discussion of “natural forces” illuminates the phenomena of nature, technology, and science.

Today, my brother Jon Gilbert addresses problems of income inequality and rising costs of living in the USA. In the first half of this episode, Jon presents figures showing the problem as it has manifested since the 1950s. Jon then argues that we may address the problem with a market-based solution: the use of "labor coins" to remove taxes on costs of living and transfer taxes to more frivolous expenditures. This is an interesting idea that I have never heard before, but which seems to mesh with various trends including the use of cryptocurrency and former experiments with dual-currency economic systems. We discuss the potential benefits of this solution in streamlining government bureaucracy and promoting equitable outcomes for full-time workers. Jon also discusses the possible caveats of the approach and the necessity of experiments that would assess whether the system works and how to fine tune it.