Michael Levin has been building a vocabulary. His recent glossary post (Substack, December 2025) and the preprint “Ingressing Minds” lay out a conceptual toolkit that, taken together, amounts to something like a research program disguised as a dictionary.

What I want to do here is walk through a few of these terms, particularly the ones that resonate with (and productively diverge from) David Krakauer’s framing of complexity science as the study of teleonomic matter. Krakauer, as president of the Santa Fe Institute, has been making a related but distinct move: arguing that complexity science’s proper domain is “matter with purpose,” or what he and Christopher Kempes call “problem-solving matter” in their 2024 essay.

Both researchers are circling similar questions. Where they land is instructive for anyone trying to think seriously about agency, intelligence, and what it means to study systems that have agendas.

Recommended listening: if there’s one thing you want to focus on when trying to comprehend Krakauer’s views on complexity, I’d suggest Sean Carroll’s conversation with David Krakauer on Mindscapes.1 It’s a few years old, but is a decent pace of history, context, theory, and useful questions from Carroll.

Agential Material and Teleonomic Matter

Start with Levin’s term agential material. This is the stuff engineers work with that has “a significant degree of autonomy, an agenda, perhaps homeostatic capacity or higher, which it will execute independently.” The degree to which your material is agential, Levin argues, is the degree to which engineering is really reverse-engineering, and the result is a collaboration with the material rather than an imposition on it (Levin, “Darwin’s Agential Materials,” Cell Mol Life Sci, 2023).

Krakauer’s teleonomic matter covers overlapping territory. In his conversation with Sean Carroll, Krakauer defined complexity science’s ontological domain as “matter with purpose,” distinguishing it from the ordinary2 matter studied by physics (Krakauer on Sean Carroll’s Mindscape, Episode 242, July 2023). He locates the origins of this field in the Industrial Revolution, the era when machines (both human-made and evolved) forced a reckoning with purposeful systems. Krakauer writes that complexity science and machine learning both target “teleonomic/purposeful matter,” systems that “encode historical data sets for the purposes of adaptive decision-making” (Krakauer, “Unifying Complexity Science and Machine Learning,” Frontiers in Complex Systems, 2023).

The overlap is real, but the divergence is where things get interesting. Krakauer’s framing stays closer to a computational and information-theoretic register. His teleonomic matter is defined by its capacity to encode, process, and act on information in service of goals. Levin’s agential material pushes further: it emphasizes that the material itself has competencies, that it actively problem-solves in ways that may surprise the engineer, and that the optimal strategy for interacting with it is behavior-shaping intervention rather than micromanagement.3

Levin’s framing also carries an implicit ethical weight4 that Krakauer’s doesn’t quite reach. If the material you work with has an agenda, you are in a relationship with it. A recent preprint Levin co-authored with Richard Watson makes this point explicit:

the inadequacy of the standard machine metaphor5 in biology stems from treating cognition as something that appears above a complexity threshold, rather than as a continuum present at every scale

(Levin & Watson, “Machines All the Way Up and Cognition All the Way Down,” Seminars in Cell & Developmental Biology, 2026).6

The Axis of Persuadability and the Spectrum of Interventions

Levin’s axis of persuadability organizes systems from mechanical clocks to humans along a spectrum defined by what kind of intervention is optimal for prediction and control: rewiring, setpoint editing, training, logical arguments. It’s “an engineering take on the question of agency, designed to bring deep philosophical questions into tight contact with experimental science” (Levin, “Technological Approach to Mind Everywhere (TAME),” Frontiers in Systems Neuroscience, 2022).

Krakauer arrives at a related insight from the complexity side. He argues that the key question is what kind of theory is appropriate for a given system. As he puts it: “Imagine how hard physics would be if particles could think. That is essentially the essence of complexity” (Krakauer, Mindscape 242). Both are saying that the nature of the system should determine the toolkit you bring to it. But Levin makes this operational in a way that Krakauer hasn’t quite matched: the axis of persuadability is a practical guide for experimentalists. It asks, concretely, what kind of approach will persuade this system to do what you want it to do? That question changes the engineer’s posture from one of command to one of negotiation.

Teleophobia: The Cost of Undershooting

Levin’s coinage teleophobia names something that most researchers working at the boundary of biology and philosophy have felt but rarely articulated: “the unwarranted fear of erring on the side of hypothesizing too much agency in explaining or predicting the behavior of a system.” The term identifies an asymmetry in how the field treats errors. Attributing too much agency is seen as a grievous intellectual sin (anthropomorphism, vitalism, softness). Attributing too little is treated as the conservative default, a respectable position. Levin argues this is backwards. The failure to recognize competencies in a system is just as much an error as over-attribution, with real costs for both understanding and engineering7.

Worth noting in that context: this connects to Krakauer’s own argument about the limits of reductionism. In his Frontiers paper, Krakauer invokes Philip Anderson’s “More Is Different” from 1972 to argue that the accumulation of broken symmetries in complex systems means that the laws of physics lose much of their explanatory power at higher scales. The emergentist position, as both Krakauer and Levin have observed, often functions as a placeholder: we say “emergence” and stop asking questions. Levin’s teleophobia names the cultural mechanism that keeps that placeholder in place. It’s the reason researchers reach for mechanical explanations even when the system is demonstrably doing something more interesting.8

The Platonic Space: Where Levin Goes Further

In “Ingressing Minds,” Levin proposes that patterns of form and behavior ingress from a structured, ordered, non-physical Platonic space. This space contains low-agency patterns like facts about triangles and prime numbers, but also higher-agency ones: behavioral competencies, kinds of minds. This is a genuinely radical claim, and Levin knows it. He writes on his blog that the paper “holds the record so far” for the most speculative thing he’s published, while noting that the ideas are “very much in flux.”

The empirical motivation is substantial, though. Levin points to planarian flatworms that regenerate heads of other species without genetic change (by disrupting bioelectric circuits), and to kidney tubule cells that restructure themselves around novel constraints in ways no molecular pathway explicitly encodes. The “Ingressing Minds” preprint sketches a research program built around synthetic biology and bioengineered constructs (Xenobots, Anthrobots, chimeras) as “exploration vehicles” for mapping the structure of this space (Levin, “Ingressing Minds,” PsyArXiv, 2025).

Krakauer may resist this framing.9 His take on complexity science stays within the materialist paradigm, even as it insists on the irreducibility of higher-level descriptions. He invokes Anderson’s broken symmetries and the concept of effective theories from physics: you don’t need to know what neurons are doing to study cognition. Levin is arguing for something stronger than emergence-as-effective-theory. He’s arguing that the patterns themselves have a kind of reality, that the Platonic space is structured and explorable, and that physical embodiments function as pointers or interfaces into that space.

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Whether or not you buy this “metaphysics”, the research program it generates is concrete. Levin’s lab (and of course Bongard’s work as well) is already using biobots and bioelectric interventions to probe the space of possible forms in exactly the way the framework suggests.

The question worth sitting with is whether treating the Platonic space as real produces better science than treating it as a useful fiction. Levin’s own stated criterion, which I find compelling as a heuristic: he focuses on “forward-looking fecundity of research programs” over “philosophical precommitments such as physicalism or reductionism.”

The Research Program: Pointers, Interfaces, and the Adjacent Possible

The most concrete part of “Ingressing Minds” is the research agenda it outlines, which has two main thrusts.

The first is studying the “adjacent possible” around existing forms. Xenobots teach us about patterns adjacent to those of frog embryos; Anthrobots teach us about patterns adjacent to adult human tissues. By creating synthetic constructs that have no evolutionary history as such, researchers can study what patterns emerge from a given set of biological hardware when freed from the defaults.

The second is investigating minimal systems: simple sorting algorithms, chemical droplet robots, gene regulatory network models. In systems where every component is known, any unexpected competency (like the “delayed gratification” Levin’s group found in sorting algorithms) is a genuine signal about what the system can access, with no hidden mechanisms to appeal to (Zhang, Goldstein, & Levin, “Classical sorting algorithms as a model of morphogenesis,” Adaptive Behavior, 2024). Recent work on associative conditioning in gene regulatory networks extends this approach, showing that even simple regulatory architectures can acquire learned associations that increase their causal integration (Pigozzi, Goldstein, & Levin, “Associative Conditioning in Gene Regulatory Network Models Increases Integrative Causal Emergence,” OSF Preprints, 2024).

This second thrust has a resonance with Krakauer’s own research program. Krakauer has argued that computation “does not emerge from silicon, tungsten, insect excreta or other materials” but from “procedures of reason or logic” (Krakauer & Kempes, “Problem-Solving Matter,” Aeon, 2024). Both are arguing, in different registers, that the interesting properties of complex systems transcend their substrates. Krakauer frames this as computation being substrate-independent; Levin frames it as patterns ingressing through physical interfaces. The empirical predictions may be closer than the metaphysics would suggest.

The Selflet and What It Means for Continuity

One more term worth sitting with. A selflet, in Levin’s vocabulary, is a thin temporal slice of a cognitive being, measured in hundreds of milliseconds for a human. The concept emphasizes that cognitive agents are dynamic patterns that reconstruct themselves and their past from currently available memory at each moment. On this model, memories are messages from your past self, and actions constrain and enable your future selves by reshaping the landscape of available options.

Levin developed this idea at length in “Self-Improvising Memory” (Levin, “Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue,” Entropy, 2024), where he argues that memory’s function is to preserve salience rather than fidelity. The persistence of any cognitive system, from a cell to a society, depends on continuous creative reinterpretation of stored patterns. What looks like faithful recall is actually improvisation constrained by the architecture of the system and the structure of the information it carries.

This is a reframing with practical consequences. It dissolves familiar philosophical puzzles about personal identity into engineering questions about how patterns persist and propagate across temporal boundaries. It also connects, in ways I’m still thinking through, to questions about how organizations and research fields maintain continuity.

How does a collective intelligence (whether a body, a lab,10 or a field) sustain coherent goals across time when its components are constantly turning over? The selflet concept suggests that the answer has less to do with stable storage and more to do with the quality of the improvisational process at each moment of reconstruction.

Where This Leaves Us

Krakauer gives us the institutional and methodological frame: complexity science as the study of teleonomic matter, with its own history, its own theoretical commitments, and its own relationship to the physical sciences.11 Levin gives us a vocabulary for the empirical frontier, a set of terms designed to make previously invisible phenomena visible and experimentally accessible. Both are insisting that the tools we inherited from physics are insufficient for the systems we now need to understand.

The productive tension between the two is what I want to sit with across this series. Krakauer’s materialist complexity science provides discipline: it demands that claims about agency and purpose be cashed out in terms of effective theories and empirical predictions. Levin’s willingness to venture beyond physicalism provides ambition: it asks whether the framework can be expanded to accommodate phenomena that the standard account struggles with. Neither alone is sufficient, and I’m not yet convinced they fully reconcile.

The next post will work through the Carroll-Krakauer conversation in more detail, particularly Krakauer’s argument that complexity is not pre-paradigmatic and what that commitment costs. Subsequent pieces will trace where the two frameworks converge on information processing as the unit of analysis, where they diverge on whether substrate matters, and what an experimentalist working in the Levin tradition might actually borrow from the SFI vocabulary (and vice versa).

If Levin is right that the Platonic space is explorable, then complexity science will need new tools for characterizing what the exploration finds. If Krakauer is right that effective theories12 are all we need, then Levin’s vocabulary will have to earn its keep by generating predictions that materialist accounts can’t match. Either way, the work of the next decade in this space will be done by people who can hold both frames at once long enough to test them against each other.

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Appendix: Levin’s Definitions

These are selected direct quotes from ’s substack:

• Agential material - the subject of engineering (by evolution or by human engineers or by cells or whatever) which is not passive and not even just active or computational, but has a significant degree of autonomy - an agenda, perhaps homeostatic capacity or higher, which it will execute independently, and which serves as the target of behavior-shaping interventions (not micromanagement) in optimal control. As an engineer, the degree to which your material is agential is, practically, the degree to which you need to account for, and can exploit, its autonomy; it’s the de gree to which engineering is really reverse-engineering, and the degree to which the result os a collaboration with the material. Living matter is a multi-scale agential material, with learning capacity and goal-seeking competencies at every scale, which has massive implications for evolution, bioengineering, and biomedicine. But even non-living, minimal constructs can exhibit some of this.

• Axis of persuadability - a spectrum containing different kinds of systems (from mechanical clocks to humans and beyond) that organizes them with respect to what kind of interventions (rewiring, setpoint editing, training, logical arguments, etc.) are optimal for prediction and control of that system. The question is, what kind of approach is needed to persuade the system to do what you want it to do. It’s an engineering take on the question of agency, designed to bring deep philosophical questions into tight contact with experimental science and discovery. See Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and minds.

• Platonic space - a non-physical latent space of patterns (such as observed in mathematical objects) which, while not determined by evolutionary history or properties of the physical world, affects physical events and in particular is heavily exploited by evolution (i.e., physics is constrained by these patterns, while biology is what we call the study of systems that are enabled by them). “Platonic space” as a term is used not to necessarily hew close to the views of Plato, Pythagoras, and many others who thought about this issue, but to make a link to the project of Platonist Mathematicians who see themselves as not creating novel structures but discovering pre-existing patterns in an ordered space. I envision a broader latent space which contains not only the (seemingly) low-agency facts of mathematics but more complex, higher-agency patterns we recognize as behavioral competencies (a.k.a., kinds of minds). The causal influence of mathematical facts on the physical world suggests a kind of interactionism: that the mind:brain relationship is symmetrical to the relationship between mathematical objects and physical ones. I propose a research program using synthetic interfaces to those patterns (including biobots and hybrids that do not have a specific evolutionary history to explain their form and behavior) to work out the structure of the space and the mapping between the causal architecture and other properties of physical embodiments (cells, machines, embryos, etc.) and the patterns that ingress into the physical world through them. As a corollary to this view, we can define mathematics as the branch of cognitive science that studies the behavior of those Platonic space denizens whose behavior can be precisely and sharply defined, while computer science and biology study the behavioral properties of more agential, intelligent patterns as observed through specific kinds of dynamic and living interfaces respectively.

• Selflet - a Selflet refers to a thin temporal slice of a cognitive being (in a human, it would be measured in hundreds of milliseconds) across time, as in the “space-time bread loaf” of Special Relativity. It emphasizes that cognitive agents like us are not static, perduring entities but a dynamic pattern that has to re-construct itself and its past from the currently-available memory engrams in its brain, body, and environment. Selflets are snapshots of a mind’s “now” moment, and some large number of Selflets integrate into what looks, to observers (and to itself), as an entire agent lasting through time. This model facilitates thinking about memories as messages from your past self, and actions as constraining and enabling your future selves by deforming the energy landscape of the options available to you in the future (via actions that change environmental features and your own structure and information content). This model thus exploits similarities between lateral interactions between agents and “vertical” interactions between a single agent’s past and future selves.

• Teleophobia - the unwarranted fear of erring on the side of hypothesizing too much agency in explaining or predicting the behavior of a system, and the usage of conceptual tools and strategies that are best suited for the most mechanical side of the spectrum (coupled with insufficient concern about incorrectly attributing too little). This limiting perspective usually arises from a mistaken belief that agentic properties in a system can be adjudicated linguistically or philosophically, and from a failure to embrace the tools that have been available for decades to rigorously study and empirically discover tractable levels of competency in any given system.13

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Most conversations about longevity begin in the lab—or at least, they pretend to. We hear talk of lifespan extension, senescence, gene therapies, epigenetic clocks. But these speculative narratives often race ahead of the research itself. The public discourse paints a picture of boundless possibility, while the actual science remains cautious, fragmented, and unevenly applied.

Longevity R&D aims to stretch the timeline. But flourishing and progress depends on what we do with the added space. A longer life is not necessarily a better life—especially if our systems, institutions, and social contracts remain built for shorter, more linear, and more fragmented timelines.

We are entering an era where biological breakthroughs, artificial intelligence, and social upheaval are converging. The future of health will not only be determined by what we can do to the body, but by what we know how to do with each other—how we learn, mentor, coordinate, build, adapt, and sustain direction in an age where everything is speeding up and breaking down.

If we are to meet this moment, we must expand our definition of longevity. We need more than cellular rejuvenation—we need cognitive durability, institutional relevance, and intergenerational regeneration. We need a culture and infrastructure that supports long-term flourishing: of minds, missions, and communities.

This essay is a call to reimagine longevity not just as a biological feat, but as a design challenge. It’s about building the scaffolding—personal, interpersonal, and systemic—that enables us to remain adaptive, coherent, and humane in a 100-year world. Drawing on lessons from healthcare and health technology, mentorship ecosystems, organizational strategy, and interdisciplinary innovation, I suggest that the most important longevity technologies might not come from a petri dish—but from how we shape people, programs, and paradigms for endurance, not just expansion.

Here, I explore what it means to build not just longer lives, but long-term coherence: in our systems, selves, and societies. I argue that supporting what I call the Longevity Generation—those who will live through and shape this century’s transformation—requires a new kind of compass. We need not just direction, but what I call Destination Discernment: the ability to chart meaningful trajectories through complexity; and in this case, development, care, and innovation through social, institutional, and technological complexity—without losing sight of what truly matters.

II. What Longevity Leaves Out

Three Metaphors for Misalignment

A few metaphors and analogy to situate the forthcoming discussion:

Imagine cultivating a rare, long-living plant species—but in soil that’s depleted, in a climate that’s volatile, with no gardeners to tend it. That’s what our current longevity paradigm risks: biological extension in an ecosystem unprepared to sustain it.

In software, an infinite loop is a failure of logic—a process that repeats without progress. We risk the same with longevity if we extend time without redesigning the systems that give it shape, function, and exit conditions.

More time isn’t always a gift—it can be a maze without a map. We’re extending lifespans but not equipping people or institutions with the tools to navigate those added years with clarity, purpose, or coherence.

A Brief Story

A few years ago, I was sitting in a meeting with R&D leads and executives at a healthcare technology company, discussing the roadmap for our next generation of monitoring tools. On paper, we were making progress: better sensors, more robust data pipelines, even early integration with predictive AI systems. But something felt missing. In our push to optimize what we could measure, we had neglected to ask what really matters in the long arc of human health.

Securing buy-in around what should even be considered—or offered—was difficult. There was (and generally is) a persistent tension between what the market was ready to adopt, what human well-being actually required, and what was even close to scalable.

This is a pattern I’ve seen across biotech and innovation spaces: a fixation on the molecular and the mechanical, with little room for the mental, social, and ecological. There are parallels and overlaps with other major philosophical matters as well: the longstanding rift between reductionism and complexity (Fraser & Greenhalgh, 2001; Noble, 2006); and even in a more applied sense, the role of embodiment in cognitive sciences (Varela, Thompson & Rosch, 1991; Shapiro, 2010).

Longevity gets framed as a problem of biology alone—extend the telomeres, slow the senescence, fix the protein folding. Rarely do we ask: What kind of life are we extending? And for whom?

We treat health as a personal variable, but it is deeply shaped by systemic forces—educational opportunity, mentorship access, social scaffolding, narrative framing. I’ve mentored dozens of early-career researchers and technologists through JOPRO, Orthogonal Research and Education Lab, Google Summer of Code, and now From Here to There: Strategy and Mentorship for Innovators and Data x Direction, and what I see most often isn’t a lack of intellect or drive—it’s burnout, decision fatigue, disorientation. Not just biological stress, but strategic and spiritual depletion. These too are longevity challenges. Research on burnout and stress (Peters, McEwen, & Friston, 2017) and the classic work of Christina Maslach underscore how chronic overload erodes capacity.

If we build medical interventions without parallel social and institutional designs, we risk extending time without meaning. Healthspan isn’t just about vitality metrics—it’s about being able to contribute, connect, and navigate life with coherence. It’s about flourishing in motion, not just surviving in stasis. This broader lens aligns with research on the social determinants of health (Marmot, 2005).

The current paradigm of longevity is siloed and shortsighted. We need a broader lens—one that acknowledges that real longevity is a social and strategic achievement, not just a biological one.

III. Mentorship as Infrastructure

When we think about infrastructure, we usually picture roads, bridges, power grids—the physical systems that hold up our daily lives. But in the context of a longevity-focused society, mentorship and intentional cultivations of intra- and inter-personal skillsets may be one of the most underappreciated forms of infrastructure we have.

Mentorship is not just a nicety or a professional development perk. It is a mechanism of resilience, a means of knowledge transfer, a system for emotional and strategic co-regulation across generations. It provides the social scaffolding that allows individuals and communities to orient themselves toward long-term goals, especially in volatile and uncertain contexts.

Through my various roles, I’ve mentored early-career scientists, technologists, and interdisciplinary thinkers navigating fields that often lack clear roadmaps. What I’ve found is that mentorship done well doesn’t just pass along information—it cultivates discernment, direction, and durability. It helps people manage complexity, interpret failure, and sustain coherence over time.

These are far beyond “soft skills” that offer tertiary levels of enhancement. They are survival traits in a world where timelines stretch, decisions compound, and crises multiply. This is something Douglas Rushkoff explores in Present Shock: When Everything Happens Now—a powerful diagnosis of our cultural breakdown under temporal disorientation. Rushkoff argues that our collective inability to inhabit coherent timelines has created a crisis of meaning, purpose, and continuity. In a longevity context, this insight is critical: as our lifespans extend, so too must our ability to make sense across longer arcs. Mentorship, in this frame, becomes not just career guidance—but narrative restoration.

In biotech, in AI, in health innovation, mentorship and destination discernment —our ability to develop advances in technology to viable destinations— are what connects the speed of technical development with the wisdom required for ethical deployment. Destination discernment—echoes calls from long-term thinkers like Stewart Brand and Roman Krznaric, who remind us that future-fluent action demands tools for anchoring foresight in action, not just prediction. These are, in effect, a public health intervention—one that shapes not just individual careers, but the collective trajectory of fields poised to redefine life itself.

To mentor the longevity generation is to design for systemic continuity. It means building pipelines that don’t just prepare young people to enter today’s institutions—but equip them to adapt, critique, and evolve those institutions for a future we can’t fully predict. It means treating mentoring not as episodic advice, but as a design principle embedded into the DNA of innovation ecosystems.

And it means recognizing that every time we mentor well, we don’t just extend someone’s opportunity—we extend the timeline of what’s possible for all of us.

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IV. Cognitive Longevity: Leading Without Burning Out

If biological longevity is about extending the body’s capacity, cognitive longevity is about extending the mind’s. This includes not just preserving memory or preventing burnout, but cultivating the capacity to lead, learn, and make decisions across longer, more complex timelines.

In an era defined by information overload, increasing volatility, and accelerating change, decision-making itself is under threat. Many leaders—whether in science, business, or public service—are stretched thin by fragmentation and fatigue. Cognitive longevity means developing habits, frameworks, and institutional practices that allow people to sustain direction, coherence, and creative agency over decades, not just quarters.

As OpenAI CEO Sam Altman recently noted in a discussion on preparing for the future, beyond learning technical tools, the core traits that will define success are adaptability, resilience, and the ability to discern what others truly value. These “super learnable” skills, as he calls them, are not only crucial for navigating the near-term impacts of AI—they are essential for long-range directionality in a volatile world. In his words: “When AI can kind of do anything… then deciding what to do and what people value is going to be really important.”

Cognitive load theory, originally developed in educational psychology, highlights the importance of working memory and schema development in managing mental complexity. But its lessons are rarely applied to leadership or institutional design. Similarly, the literature on adaptive expertise (Hatano & Inagaki, 1986; Schwartz et al., 2005) shows that expert practitioners don’t just repeat learned solutions—they know when to pivot, adapt, and innovate. This kind of flexible intelligence is precisely what longevity demands.

In my work across health tech & healthcare, innovation ecosystems, and consulting and mentoring at large, I’ve seen how the absence of cognitive sustainability creates cascading failures—good projects stall, good people burn out, and institutional knowledge dissipates. At scale, this becomes an innovation bottleneck.

We need environments that actively support cognitive renewal: sabbatical structures, mentorship webs, reflection loops, and epistemic humility baked into governance. The best organizations invest not only in what people know, but in how they regenerate clarity and discernment over time. Think of this as the organizational equivalent of mitochondrial health.

The stakes are high. In domains like AI, biotech, and public health, short-term thinking can produce irreversible consequences. Developing cognitive longevity is, in this sense, a form of ethical risk management. As the philosopher Norbert Wiener warned in the mid-20th century, our technical power will always outpace our capacity to govern it—unless we deliberately cultivate foresight and responsibility.

To thrive in a 100-year world, we need minds that can think in 100-year arcs. That means leaders who can hold paradox, metabolize uncertainty, and operate with sustained attention in an age of distraction. It also means treating wisdom not as a byproduct of aging, but as a practice we can design for—and a critical input for human flourishing.

V. Living Systems and Institutional Healthspan

If people are living longer, our institutions must learn to do the same—not through stagnation, but through adaptive vitality. Yet most organizations age poorly. They calcify. They forget. They resist renewal. To design for true longevity, we need to treat institutions not as static bureaucracies, but as living systems.

This shift requires a new ontology of organizational life. Just as living organisms metabolize resources, process feedback, and repair themselves, resilient institutions must also sense, learn, adapt, and regenerate. The fields of complexity science and cybernetics offer powerful metaphors here: feedback loops, homeostasis, self-organization, distributed intelligence. These are not buzzwords but rather they’re the operational grammar of long-term relevance.

At Orthogonal Research and in collaborations with DevoWorm (a project under the OpenWorm Foundation), we’ve explored how principles from systems biology and morphogenesis can inform institutional behavior. One insight: aging institutions often lose the ability to integrate novelty without chaos. Like overburdened biological systems, they become inflamed, rigid, and brittle—what in human terms might resemble autoimmune response. What’s more, an ongoing Google Summer of Code project series is about Open Source Sustainability, which specifically addresses the challenges of whether open source projects have an enduring link to the rest of the software economy - or not.

To extend institutional healthspan, we must cultivate "meta-mentorship": not just mentoring individuals, but mentoring the environments and processes that shape how knowledge moves and decisions evolve. This includes modular design, pluralistic governance, and regenerative cadence—space for pause, reflection, and reconfiguration. In our Society Ethics Technology working group, we are exploring how values-forward design and decentralized governance protocols can strengthen epistemic resilience at scale.

This is also where destination discernment reappears—not just as an individual capacity, but as an institutional function. Projects like FrontierMap are designed to scaffold the navigation of complex research terrains, helping teams and organizations discover not only what's emerging, but where it’s actually worth going. In institutional contexts, this means knowing how to sunset, pivot, or evolve responsibly—rather than chasing relevance indefinitely.

Examples exist. The Santa Fe Institute models how intellectual ecosystems can evolve across decades. The Metagov Project explores programmable governance. The Long Now Foundation builds literal and metaphorical infrastructure for multi-century thinking. These aren’t fringe efforts—they’re testbeds for what it means to embed longevity into the DNA of our social systems.

Ultimately, we must stop asking only how long people can live—and start asking what kinds of environments we need to age well together. Institutional healthspan is the invisible architecture behind every meaningful long-term effort. Without it, we extend lifespan into systems too fragile to hold it.

VI. Biotech Needs a Compass

Biotech is often framed as the frontier of human enhancement—the most literal domain of longevity science. From gene editing to neurostimulation, we are rapidly gaining the ability to alter the substrates of life. But the speed of possibility is outpacing our frameworks for meaning. In this space especially, we need a compass, not just a map.

As I’ve written elsewhere, technological trajectories without destination discernment risk becoming expressions of capability rather than contributions to human flourishing. Just because we can doesn’t mean we should—and even when we should, we must still ask: toward what end? And for whom?

The current biotech narrative is missing a critical scaffolding: the moral, narrative, and governance infrastructure needed to guide not only what we build, but how we relate to it. We lack coherent stories of where we are going, or what we want from our tools. Ethical review boards and regulatory frameworks are necessary—but they’re not sufficient. We need cultural architectures of intentionality.

This perspective also resonates with emerging work on diverse forms of intelligence—biological, artificial, or hybrid. As biotechnological capabilities expand, so too does the imperative to rethink agency and ethics. Michael Levin, whose work bridges developmental biology and synthetic morphogenesis, notes that the goal is "to provide a naturalized, continuous view of cognition across its spectrum [...] toward the development of ethical/social frameworks that will become essential in the future as the forms of agents around us diversify far beyond what is currently imaginable."

In other words, the future of biotech won’t just reshape biology—it will demand that we reshape how we conceive of agency, empathy, and responsibility. The ethical frontier is expanding, and our compass must be capable of pointing toward futures that include, rather than marginalize, the full spectrum of sentient and semi-sentient life. This is Destination Discernment at its broadest scale: not just where our tools go, but who we include in the future we’re aiming for.

This is especially urgent in longevity science, where interventions are marketed in terms of personal optimization but enacted within systemic inequity. Scholars have increasingly called attention to how medical access, algorithmic bias, and even definitions of 'health' often reflect privileged norms. Critical disability studies, for example, highlights how mainstream health interventions can erase the needs and insights of neurodivergent, disabled, or chronically ill communities. Researchers in fields like STS (Science and Technology Studies) and feminist technoscience have emphasized the need to center pluralism in our visions of human flourishing. See works such as Alondra Nelson’s "Body and Soul", or the interdisciplinary project AI Now, which addresses social inequities embedded in emerging technology. What values are embedded in a lifespan-oriented society? Who defines vitality? What constitutes a worthy extension of life—and who gets access to it?

To fill these gaps, we need a new generation of transdisciplinary translators—people who can connect the hard tissue of technology to the soft tissue of values, narrative, and social design. This is part of what we aim to model in our JOPRO mentorship and strategy efforts: helping researchers and innovators locate their work within larger arcs of purpose.

It’s also what animates a new wave of organizations like The Roots of Progress and The Cosmos Institute, which attempt to reclaim a positive, human-centered and narrative of technological development, as well as affirming the moral imperative of tending to progress, and establishing the Philosophy to AI Pipeline. The goal isn’t to slow down innovation, but to shape it. Progress is not just acceleration—it’s alignment.

In an age of powerful tools, the ethical frontier is design. And in the longevity era, the compass we need most is not pointing us faster into the future—it’s helping us hold direction as we go.

VII. Vignettes and Personal Examples

Theories are sharpened by proximity to lived experience. Much of what I’ve argued throughout this essay comes not just from reading and reflection, but from being embedded in interdisciplinary, innovation-heavy environments where real people confront real limitations—and try to transcend them.

In my work in the health domain, I sat in cross-functional meetings where machine learning engineers, hardware developers, and clinicians wrestled with trade-offs between feasibility, scalability, and meaning. On one hand, we could build increasingly sophisticated tools for niche patient monitoring and wellbeing; on the other, we had to contend with messy realities: user fatigue, accessibility gaps, technological buy-in, and regulatory uncertainty, just to name a few. I watched how cognitive overload wasn’t just an individual issue—it spread through teams and leadership, subtly shaping what got prioritized and what quietly fell off the roadmap.

Through my various roles, I’ve mentored early-career researchers, mid-level managers, and even advised and consulted with executives—many of them navigating emerging fields like data ethics & responsible AI; updating psychology and neuroscience to incorporate advances in mental health and neurodivergence; developmental biology and other STEM fields meshing with complex systems and metascience. Again and again, I’ve seen how mentorship and destination discernment function not just as guidance but as grounding: stabilizing forces that lets people sustain direction when no clear institutional lane exists. These moments of “holding steady” matter just as much as breakthrough moments. They are the socio-emotional infrastructure of innovation.

Even more personally, I’ve watched people close to me—family, friends, collaborators—grapple with what it means to live a meaningful life under chronic illness, economic precarity, or cultural misrecognition. These aren’t footnotes to the longevity conversation; they’re central. They illuminate the distance between biological extension and systemic support.

This arena of work I’m attempting to demarcate via this essay is, in many ways, an attempt to honor that distance. And to suggest that closing it will take not just progress in technology, but progress in how we care, coordinate, and continue.

VIII. Conclusion: The Long Now, the Living Future

If we are truly entering an age of longer lives, then longevity is no longer a biological project alone—it is a civilizational one. The challenge ahead is not simply how to extend life, but how to ensure that what we extend is worth living: mentally coherent, socially supported, and morally navigable.

This requires rethinking how we build, lead, and relate. It means investing not just in innovation, but in narrative. Not just in science, but in stewardship. It calls on us to design for the long now: to support projects, institutions, and people that can metabolize change while maintaining a sense of purpose and direction.

I’ve tried in this essay to sketch the outlines of that challenge: mentorship as infrastructure, institutions as living systems, cognitive longevity as an ethical imperative. These aren’t abstract ideals. They are design problems—ones that can be met with creativity, care, and coordination.

To move forward, we must fund and elevate people and programs that make long-term flourishing possible. We must normalize reflection as a skillset, embed mentorship at every level of innovation, and build tools that are not just faster or smarter—but wiser. This isn’t about utopia. It’s about sustainability, dignity, and direction.

The question is no longer just: How long can we live? The real question is: How do we design a future where we want to keep going?

This is the work of our time. Let’s make it count.

On May 1st, we attended the Social and Ethical Responsibilities of Computing (SERC) Symposium at MIT's Schwarzman College of Computing — a full-day series of rapid-fire, TED-style talks from researchers working at the edges of data ethics, AI governance, civic technology, and digital justice. What emerged was a mosaic of projects that not only challenge the current state of computing, but push us to ask: What should we be building instead?

Below are some highlights and takeaways — from algorithmic monocultures to deliberative democracy platforms, the day was a reminder of both how far we’ve come, and how much further we have to go.

Data x Direction

Notes: MIT's Social and Ethical Responsibilities of Computing Symposium 2025

On May 1st, we attended the Social and Ethical Responsibilities of Computing (SERC) Symposium at MIT's Schwarzman College of Computing — a full-day series of rapid-fire, TED-style talks from researchers working at the edges of data ethics, AI governance, civic technology, and digital justice. What emerged was a mosaic of projects that not onl…

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a year ago · 2 likes · Jesse Parent and Data x Direction

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In this post from Data x Direction, we explore the concept of Data Nutrition Labels—tools designed to promote transparency, trust, and accountability in datasets. Much like food labels, these frameworks help researchers, developers, and policymakers evaluate the quality, composition, and context of the data they use.

Data x Direction

Data Tools: Assessing Datasets with Data Nutrition Labels

This is part of Data Tools You Can Use, a Data x Direction series exploring practical tools that help data scientists and leaders build more responsible, reproducible, and ethical data projects. The goal is to bridge the gap between theoretical or academic frameworks and the real-world tools data teams actually rely on…

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a year ago · 2 likes · Jesse Parent

We review some recent examples and discuss what a more standardized, practical approach to "data transparency" might look like—especially in high-stakes or socially impactful applications.

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🧭 This piece is part of our ongoing effort to bridge data science and social responsibility by exploring the tools that shape how decisions—and futures—are built.

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The 1943 paper “Behavior, Purpose and Teleology” was a response to the prevailing scholarly attitudes that dismissed teleological explanations as unscientific and obsolete. By redefining purpose in terms of feedback, Rosenblueth, Wiener, and Bigelow sought to rehabilitate the concept of teleology, striving towards a rigorous framework that could account for goal-directed behavior in both biological organisms and machines. 

Through challenging the mechanistic orthodoxy of their time, the authors laid the groundwork for cybernetics and influenced the development of multiple disciplines that continue to explore the intricate balance between mechanism and purpose in complex systems. We will take a deeper dive into the context and content of the paper in this discussion.

Motivation Behind The Paper

1: Selecting for predictive, complex behavior 

The authors acknowledge their categorical classification is generically made so as to filter out the non-class; they also remark on how other divisions are possible to varying ends. Bradly Alicea discusses this in the context of predictive processing vs. enactivism, and also a deeper dive on this paper during a recent Saturday Morning Neurosim meeting. 

Prediction is seen as interesting as it allows for differentiation of potential “orders” of predictive behavior; the authors mention various examples of how complex or multi-dimensional certain actions may be, as we discuss further below.

2: Historical context in reclaiming a teleological discourse via secular pivot

“Teleology” has been debated in ethical, causal and philosophical traditions for well over two millennia. Causation at large, and the concept of efficient cause in specific, transformed substantially during the Scientific Revolution (1540s-1680s), with efficient cause shifting from an integral part of a teleological framework to a central tenet of mechanistic philosophy. This shift not only redefined how natural phenomena were understood but also established new methodologies that would shape modern science, prioritizing observable interactions over inferred purposes. 

Following the Scientific Revolution, the Age of Enlightenment saw considerable movement away from Scholasticism, which dominated education in Europe the since the 12th century; mechanism would come take center stage, though not without its contention, alongside evolving Deist thought. William Paley’s divine watchmaker justification for intricate complexity would be countered by Immanuel Kant’s Critique of Judgement, confronting mystical design while allowing room for teleological concepts in interpreting biological phenomena; whereas Charles Darwin would further challenge traditional teleological reasoning with his development of natural selection and evolution. 

Also of note, Maxwell’s Demon, introduced by James Clerk Maxwell in 1867, serves as a thought experiment that challenges the second law of thermodynamics; whereas 10 years after the publishing of the 1943 Rosenblueth et al paper, Francis Crick (with collaborators) would elucidate the structure of DNA and continue a wave of mechanistic, purpose-less focus. (See Bradly’s commentary during a recent Cognition Futures discussion.) 

3: Striving for a lens of analysis across natural and artificial agents

Through the framing of feedback relative to a goal, and how behaviors can be studied from this vantage point, the authors sought to demarcate a way of studying behaviors beyond conventional segregation between biological and artificial.

From our present-day viewpoint, we can point out a more robust study of agential activities that would follow: complexity science, agent-based, neuro-inspired AI, or even aspects of the cognitive science project at large. But at the time, system, information, and control theories were still very much in development. A non-reductionist take on behavior, whose domain allowed for constructed or naturally emerging agents to be seen from a uniform view, was a broader aim, and taken up by the authors and their decedents on many fronts; “Cybernetics: Or Control and Communication in the Animal and the Machine” would be published five years later by co-author Norbert Wiener.

Assertions within the paper

In the sections below, we view some of the nuggets from within the paper itself, which range from theory and philosophy —  including the relationship of feedback to purposeful, voluntary behavior — to discussing what makes the grouping of machines and organism of substance now, and how that may change ahead. 

Behavior vs Function

The paper begins by noting that their take on behavior specifically omits structure and intrinsic organization:

In a functional analysis, as opposed to a behavioristic approach, the main goal is the intrinsic organization of the entity studied, its structure and its properties; the relations between the object and the surroundings are relatively incidental. From this definition of the behavioristic method a broad definition of behavior ensues. By behavior is meant any change of an entity with respect to its surroundings.

In discussing inputs and outputs, the authors note that their use of behavior would be far too wide in scope, unless it is restricted, unless it is classified in some form. This leads to their ultimate justification of their diagram, which is intended to allow for classification of increasingly complicated behaviors through the nature of their predictive, goal-oriented actions.

Purposeful behavior is voluntary

The authors discuss that what is selected is not the minutia or components of moving the body in the world towards a goal, rather it is the goal itself that comprises the voluntary selection associated with purpose.

“When we perform a voluntary action what we select voluntarily is a specific purpose, not a specific movement.” 

Regarding the idea that “all machines are purposeful” — this is said to be “untenable.” In the view of the authors, a clock itself is not purposeful, in spite of being designed with a purpose in mind. But in terms of how purposes is used here, there is no final state to which the clock is aspiring towards [*]. 

Teleology as purpose controlled by negative feedback

Here, we see the authors further staking their claim relative to functionality, by not only showing the significance of an agent’s relation to an environment, but also that signals “from the goal” are necessary as well.

If a goal is to be attained, some signals from the goal are necessary at some time to direct the behavior.

Negative feedback, then, offers this corrective measurement, influencing the outputs from the agent relative to achieving the goal.

Positive feed-back adds to the input signals, it does not correct them. The term feed-back is also employed in a more restricted sense to signify that the behavior of an object is controlled by the margin of error at which the object stands at a given time with reference to a relatively specific goal. The feed-back is then negative, that is, the signals from the goal are used to restrict outputs which would otherwise go beyond the goal. It is this second meaning of the term feed-back that is used here.

Additionally, a reference is made to cerebellar patients (or those with motor control trouble) and associate this to a machine’s flawed actions if it has undampened feedback. The task of drinking from a cup is employed to show how negative feedback relative to the goal (is the cup increasingly closer to the mouth, is the cup being positioned such that water can be imbibed) is what is being selected for; but in the case of the cerebellar patient, oscillatory patterns of movement increase without effective check and constraint, which in turn spills the water and inhibits the achievement of the goal. 

So, if positive feedback amplifies the input signals without affording correction, and undampened feedback distorts the signal being received from the goal-state, properly functioning negative feedback is to be seen as inseparable from the realization of a voluntary, intended goal or end state.

Prediction, extrapolation, and coordinates

The authors differentiate through examples of complexity in organisms behavior, and in noting predictive and non-predictive servomechanisms. Unicellular organisms are non-predictive; a cat tracking where a mouse will go next is predictive. 

Prediction is differentiated as requiring at least two coordinates, with one of then mandatorily being time, and at least one spatial coordinate. The authors note that the “sensory receptors of an organism, or the corresponding elements of a machine, may therefore limit the predictive behavior”, with key difference described in how a bloodhound tracking a scent is not predictive; as it is only measuring the change in magnitude of one dimensionality[**].

Contrasting via causality: Beyond historical context, the authors carefully differentiate prediction from causality, whereas causality is designated as “one-way, relatively irreversible”, and functional in relationship. Teleology and causality share only the time axis, without sharing the relation to the environment and the inherent selection of a goal that purposeful, teleological behavior necessitates. 

On future engineering, grouping Machines and Organisms together, and EGRT

The final pages reveal the authors’ vantage point on what is tenable in engineering now, relative to what may become tenable in the future. They contend that the methods are similar and may remain so depends on potential emergence of qualitative differences. (This is interesting to consider regarding what would become an orthodox metaphor in the later-developed field of cognitive science: how the mind itself is a machine, even, a computer.)

Beyond remarks about implications for the means of construction, there is an interesting statement that becomes fodder for many discussions on the nature of biology, models, maps & territory, and the like: 

[…] In future years, as the knowledge of colloids and proteins increases, future engineers may attempt the design of robots not only with a behavior, but also with a structure similar to that of a mammal. The ultimate model of a cat is of course another cat, whether it be born of still another cat or synthesized in a laboratory. [p 22]

The notion of modeling or being able to produce a model eventually ties into reference and control. Later in the development of cybernetics and eventual control and systems theories, the 1970 paper “Every good regulator of a system must be a model of that system” by R. C. Conant and W. R. Ashby would bring about a curious set of of ideas regarding the nature of managing states, or, being able to regulate, account for, or perhaps predict behaviors. 

So, in the context of our 1943 paper, we can see the vantage point of those attempting to discern what scope feasibly contains natural and constructed agents — but also an attempted disclosure of limitations or suppositions that offer awareness of the means to support the usefulness of such a scope. In doing so, they offer humility about what is accessible in their contemporary era, while surfacing insight around what an entity is comprised of relative to what its behavior is, and how models of either can be of use [***].

(Selected) modern works of interest

Within various reading groups and projects at Orthogonal Research and Education Lab, we’ve come across several ties to this paper. Below is a light sampling of those authors and their works: 

Margaret Boden: a British cognitive scientist and philosopher known for her extensive work on the nature of the mind, creativity, and artificial intelligence. In her seminal work, “Mind as Machine: A History of Cognitive Science” (2006), Boden delves into the development of cognitive science and discusses how the mind can be conceptualized as a machine. The book is intended to provide historical development of the field rather than a declaration that the correct approach of cognitive science as reduction of the mind to a mere mechanical device — though it offers that mechanistic explanations can significantly illuminate how the mind works.

Thomas Fuchs: a psychiatrist and philosopher known for his work in phenomenology and the philosophy of psychiatry. In his book, “The Ecology of the Brain: The Phenomenology and Biology of the Embodied Mind” (2018), Fuchs presents a comprehensive critique of mechanistic and reductionist approaches to understanding the mind. Instead, he advocates for an embodied and ecological perspective, emphasizing the interrelation between the brain, body, and environment; his work builds upon the Francisco Varela lineage of more holistic approaches to cognition. 

Valentino Braitenberg: the paragraphs preceding the lone figure in the paper have a particular relation to the 1986 book “Vehicles: Experiments in synthetic psychology.” A takeaway from Braitenberg’s work is less about purpose and more that intelligent-seeming behavior can arise from small differences in how an agent is designed to interpret or respond to stimuli; perhaps our authors would see it as a functionality-driven take on complexity behavior. His work would become the inspiration of the Orthogonal Research and Development Lab’s Developmental Braitenberg Vehicles project series, including “Braitenberg Vehilces as Developmental Neurostimiluation”, and continues to lend insight to contemporary matters in cognitive science and related fields.

Michael Levin: a developmental and synthetic biologist known for his research in bioelectricity, morphogenesis, and regenerative biology. This paper is a frequent occurrence in Levin’s presentations, with clear connections to its engineering insights, as well as furthering the aim of grouping organism and machine via the contemporary space of of diverse intelligences. Its concepts also extend to Levin’s work on Xenobots with Josh Bongard, their co-founded Institute for Computationally Designed Organisms, as well as their papers such as Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of Modern Science of Machine Behavior. Of a particularly wide-ranging and potentially under-attended matter, Levin frequently footnotes his presentations (and his lab’s website) with reference to the need for modernizing ethical frameworks these implications for this line of work are further realized.

David Krakauer: an evolutionary theorist and president of the Santa Fe Institute, Krakauer appears to envision a form of Complexity Science as the study of “teleonomic matter”, or, theories about things that make theories; or perhaps, the study of purposeful agents. Krakauer’s work centers more on origins of life and less-personal takes on experience, but has developed concepts such as the Information Theory of Individuality. He is particularly interested in understanding how information is processed and transmitted across different levels of biological and social organization, and how this influences the evolution of intelligence and cognition.

Further Reading and Future Writing

To conclude, a few brief comments and associated questions on ideas from our discussions on the original 1943 paper. 

• Sampling, embodiment, and constraints: In the authors’ depiction of predictive behaviors, there is discussion about the nature of the speed required to process both having a goal and “receiving” feedback from the goal in the environment. There are many deep-seated matters to consider here: with potential implications for the constraints on the domains of affordances; questions about how processing (or sampling) speed affects predictive capacities; and what is the impact of cognitive offloading further on freeing up predictive capacities? 

• “Signal from the goal” and investigating feedback: if the authors carefully demarcated their purpose-driven negative feedback as a vital part of the agent-goal communication within purposeful behavior, what does this indicate about ability to interpret or perceive such signal? 

• Prediction of hitting a target, and, predicted impact of language: how does nuance and articulation for directing movement in the physical world relate to nuance and articulation for directive movement in the social world?

Finally, here are some suggested, some intended (in our reading groups!) additional texts and concepts: 

• “What is Life?” (1944): Erwin Schrödinger’s work explored the physical aspects of living cells, discussing order, entropy, and genetic information. While not a direct critique, it contributed to the discourse on life processes and whether they could be fully explained by physics and chemistry.

• Purposeful and Non-Purposeful Behavior, a 1950 response from Rosenblueth and Wiener to Richard Taylor’s critique.

• Introduction of “Teleonomy”: To avoid metaphysical connotations, scientists like Colin Pittendrigh developed the term “teleonomy” to describe apparent purposefulness arising from natural processes.

• Second-Order Cybernetics: later cyberneticists such as Heinz von Foerster and Gordon Pask emphasized second-order cybernetics, focusing on the observer’s role and self-referential systems. They critiqued early cybernetics for not adequately addressing the complexity of living systems and cognition.

• Autopoiesis: Humberto Maturana and Francisco Varela introduced the concept of autopoiesis to describe self-producing, autonomous systems, bringing a new dimension to discussions of purpose in living organisms. 

• Teleosemantics: Philosophers like Ruth Millikan and Daniel Dennett later developed theories like teleosemantics, which attempt to naturalize teleology by explaining intentionality and mental content in evolutionary terms. Also, Dennett introduced the idea of treating entities as “intentional systems” for predictive purposes, which relates to attributing purpose in a pragmatic sense.

Footnotes 

[*] — This offers some interesting questions on the topic of the “discrete vs. continuous”, perhaps suggesting that a clock’s continuous motion disqualifies the achieving of a discrete outcome. Could one argue that the clock is designed to continuously advance to the next second of activity, thereby is a “loop” of discrete actions? What the authors are getting at in this paper, and with the goal of purposeful, teleologic, predictive behavior, seems to be focused elsewhere, reasonably so. The clock does not solicit feedback from its environment as to differentiate the selection of actions relative to a desired outcome. This is a useful point to consider the vantage point and domain the authors are demarcating. It’s also a particularly salient reference to the divine watchmaker argument, further distancing mystical implications for purpose, and instead seeking to highlight an agent’s selective capacity. 

[**] The multiple dimension requirement for prediction is interesting to consider in light of Braitenberg Vehicles. Braitenberg likened the simplistic nervous system set up as perhaps demonstrating emotions, but it is clear the vehicles do not themselves predict an outcome nor seek an end state. Relating this to the above example of a clock offers an interesting juxtaposition, one that may fit well with David Krakauer’s use of the term “teleonomic” matter or agents, and intentions for advancing complexity science. 

[***] The phrasing of modeling and cats is still used in contemporary discussions, particularly regarding biology or organismic phenomena and the theories used to parse them.

The goal is not to anthropomorphize physical systems but rather to provide a naturalized, continuous view of cognition across its spectrum by approaches grounded in developmental biology and evolution. All of this work is being synthesized towards a framework for understanding and relating to (whether by communication or control) truly diverse intelligences – a project that has implications not only for evolutionary biology and someday perhaps, exobiology, but also for development of ethical/social frameworks that will become essential in the future as the forms of agents around us diversify far beyond what is currently imaginable.

Dr. Michael Levin (emphasis added)

Michael Levin’s work in general, and particularly his professional lab website, is an inspiration on many fronts. You may also be delighted to find out he is creating a blog for less formal writings at https://thoughtforms.life.

I continue to come back to the last section on his Research page, particularly the final sentence. I think there is a retrospective (unfortunately not retroactive) impact on these understandings that highlights the lacking of previous ethical frameworks and systems as well.

It’s easier to see the granular disparity the more total alternatives and diachronic landscape that can be referenced.

From lab site of @drmichaellevin https://drmichaellevin.org/research/