Week 5b: Questions - Separation & Conflict in Innovation

[jamesallenevans]

Post your (<150 word) question for class about or inspired by the following readings:

• “Abduction and the Logic of Scientific Advance.” 2021. Eamon Duede and James Evans.
• “The Paradox of Collective Certainty in Science.” 2024. Eamon Duede and James A. Evans. Philosophy of Science. Alan Love, Ed. University of Minnesota Press.
• “Destructive creation, creative destruction, and the paradox of innovation science”. 2022. Likun Cao, Ziwen Chen, and James Evans. Sociology Compass.
• “The Geometry of Perspective”. Likun Cao, Rui Pan, James Evans

[amulya-agrawal]

Thursday’s readings emphasize how true innovations and scientific discoveries emerge when “epistemic bubbles” are broken, driving breakthroughs through colliding diverse perspectives. Innovation and scientific discovery are social processes, rather than purely cognitive ones – one cannot create a replicable discovery that is generalizable to new contexts with different data or experimental designs without exploring beyond their familiar methods and networks. Innovation requires disrupting the status quo – like we saw with Rontgen’s discovery of the X-Ray – and too much stability and consensus of known practices harms meaningful advancements. It was interesting how discoveries arise from disrupting old systems, like how Nietzsche and Marx describe. However, I question the extent to which abductive reasoning is a social process – can it be purely cognitive?

It was interesting to understand the paradox about how the more science agrees with itself, the more it risks being wrong. In terms of discovering meaning from abductive reasoning, is discovering scientific truth a result of order, where existing knowledge guides research and discoveries, or is discovering truth only possible from disorder and chaos and always having to radically reinvent what we have historically believed? What is scientific truth? Do we fear certainty more than we fear uncertainty?

[dishamohta124]

Scientific progress often depends on a paradox: collaboration increases trust and certainty within scientific communities, yet excessive consensus can lead to intellectual stagnation and undermine genuine discovery. The "Paradox of Collective Certainty" argues that as scientists become more interconnected, their work may feel more valid but becomes less replicable, forming epistemic "bubbles." Similarly, "The Social Abduction of Science" reinterprets abductive reasoning as a collective process, where breakthroughs emerge when insiders—familiar with anomalies—engage with outsiders, whose fresh perspectives help resolve them. This raises an intriguing tension: while scientific trust is essential for progress, excessive homogeneity may obstruct transformative discoveries.

Question:

If trust and collaboration are crucial for scientific advancement, yet excessive consensus stifles innovation, how might institutions like universities and funding bodies optimize scientific environments to encourage both trust and epistemic diversity? Can AI or interdisciplinary networks be leveraged to counteract the risks of scientific homogeneity?

[diegoscanlon]

Resolving some contradiction

It seems that we've continuously claimed that disruptive innovations are the ones we least expect (in this week's reading, "when innovation becomes predictable, it ceases to be an engine of novelty and change.") Yet, we also continuously talk about how AI will change our economy and society and humankind.

I'm trying to reconcile these two statements through a startup / vc background, where it feels both founders and investors are making predictions about the future. While some of these predictions are contrarian (like Peter Thiel) which would seem to support our argument that disruptive are least predictable, there are waves where the industry seems to be in agreement, such as the current frenzy to invest in AI. So, how can we say that everyone is predicting disruption in a certain field, but also say that disruption is caused by the least predictable things?

A flaw in my argument may be when the entire industry is wrong about the way technology is moving (think crypto hype-cycle) -- everyone thought it would be disruptive, but it really wasn't; thus prediction leads to non-disruption. But that would imply that I'm taking for granted that people believed the internet or software or mobile would not be disruptive (if that's the case, I'm okay with it).

Another argument might be successful startups are rejected by multiple VC when fundraising -- if the VCs knew a company would cause destruction, they would obviously invest.

• Some successful startups are rejected because they're building in a market that doesn't exist yet (they're building pre-predictable-platform shift). SpaceX. This gives merit to our disruptive / least expecting statement.
• Others successful startups are rejected because the specific problem the startup is solving isn't obvious / unaddressed -- also gives our disruptive / least expecting statement merit.
• But there are also some successful companies who have many competing term sheets or oversubscribed rounds. OpenAI. Sure, maybe not every VC in the world is vying for the deal, but wouldn't that contestation imply some predictable disruption? This statement implies though that the interest of VCs is connected to some unproven technology / platform, and not some other factor like who the founder is or what the company's traction is (which would imply the disruption has already happened).

This last point leads me to think that I'm maybe looking at the wrong moment of the technology timeline -- maybe the launch GPT-3 or 3.5 was the disruption, or maybe the research that led to the architecture of these LLMs was the disruption, and thus the creation of the technology (some technical breakthrough) is where our argument about predictability and disruption lies, not the application of disruptive technology. That differentiation may lend us some clarity, because it's not like AI is a novel concept (see image below, AI is included in Y Combinator's 2014 Request for Startups).

[e-uwatse-12]

In The Paradox of Collective Certainty in Science, Duede and Evans argue that as scientists collaborate more closely—sharing data, methods, and reinforcing each other’s findings—they develop greater trust in their collective knowledge, yet paradoxically reduce the likelihood of genuine replication and discovery. How can this paradox reshape our understanding of scientific progress, and what mechanisms might mitigate the risk of epistemic "bubbles" that limit the generalizability of new findings?

Furthermore, if scientific certainty is socially reinforced rather than purely derived from empirical validation, what does this imply for the objectivity of scientific knowledge? Could the very structures that promote efficient collaboration and rapid consensus also introduce biases that make certain research questions or methodologies less likely to be explored?

[ypan02]

In the article The Paradox of Collective Certainty in Science, the authors discuss ways to mitigate the trade-off between collective trust in the scientific community and the innovation slowdown caused by homogeneity. One policy that stood out to me was the concept of Education as Experiment. The authors propose breaking down traditional university departments and encouraging students to explore multidisciplinary studies with the hope of fostering innovative breakthroughs. This reminds me of universities where students can create their own majors and design a course load of their choosing subject to approval.

While I find this idea creative and potentially transformative, I also wonder if the administrative burden required to implement would be too much for universities to handle. More importantly, would students and employers be receptive to this model? How will the job market need to evolve in order to make this kind of interdisciplinary education effective in cultivating innovation and talent? Would employers be open to evaluating candidates based on a diverse skill set and portfolio, rather than traditional degrees? Or is there too much risk involved in moving away from well-established credentialing systems?

[kbarbarossa]

The paper The Paradox of Collective Certainty in Science argues that as scientists collaborate more closely and rely on shared data, methods, and collaborators, their collective certainty in scientific findings increases while the likelihood of genuine replication declines. This creates a paradox where increased epistemic trust leads to scientific "bubbles" that may limit true discovery.

Are there historical examples of researchers or fields that successfully resisted epistemic bubbles, and what lessons can be drawn from them?

[tHEMORAN02]

The paradox of collective certainty is an important observation that as scientists work together, they largely begin to engage in herd behavior around their findings and the chance of serious replication decreases. This is likely part of the replication crisis and is not a new trend even if it is a more new observation.

Can we observe more scientific consensus in the fields that suffer worse replication problems like psychology?

[Adrianne-Li]

The "Paradox of Collective Certainty" suggests that increased collaboration among scientists leads to greater confidence in shared knowledge while simultaneously reducing genuine replication and discovery. However, fields such as experimental physics and biomedical research rely heavily on large-scale collaboration and standardized methodologies. How do these fields manage the trade-off between epistemic trust and the risk of intellectual stagnation? Are there institutional mechanisms or funding structures that actively mitigate the downsides of excessive consensus while still maintaining the benefits of collaboration?

[jacksonvanvooren]

Cao, Chen, and Evans in “Destructive creation, creative destruction, and the paradox of innovation science” devote a section to examining how large societal disruptions, like wars, natural disasters, and economic crises, can catalyze innovation. These events are inherently destructive, breaking apart existing institutions, redistributing resources, and forcing new social and technological adaptations. Advancements in nuclear energy or medical technology during World War II illustrate how crises can drive breakthroughs. Economic downturns, moreover, can also push innovative firms to develop strategies to adapt to changing conditions.

If crises break down existing structures to create space for radical innovation, then does that suggest stability inherently suppresses groundbreaking advancements? Innovation still occurs under stability, but I wonder if that happens at a slower rate. With this in mind, how might societies balance the need for stability with the creative potential of disruption without relying on external crises?

[saniazeb8]

How do intellectual bubbles in scientific research interact with the paradox of innovation, where the very structure that enables knowledge creation also limits its ability to disrupt and evolve?

This week's readings were quite intuitive, I believe scientific progress depends on both stability and disruption, but knowledge bubbles create a paradox researchers gain confidence within insular networks, yet this limits true discovery. The replication crisis reflects this issue, where findings hold within closed groups but fail in broader contexts. Similarly, innovation loses its transformative power once it becomes predictable. If science is increasingly shaped by insular communities, does innovation itself risk stagnation? Breaking these bubbles through cross-disciplinary disruption encouraging unexpected collaborations and unconventional thinking may be key to revitalizing both scientific discovery and technological advancement.

[anishganeshram]

As scientists collaborate more, do we become less assured of their collective claims? Open data and shared methods build epistemic trust, but do they also create “epistemic bubbles” where findings seem robust simply because they rely on the same assumptions? This unity can weaken the diversity needed for true conceptual replication. The authors warn that large collaborations and open science mandates may push research toward uniformity. How can we foster collaboration while preserving independent inquiry? Striking this balance is key to maintaining scientific rigor. What research frameworks and incentives might ensure both cooperation and the heterogeneity science needs?

[LucasH22]

In "Social Abduction of Science," the authors claim that "if we want to shake loose the diversity of ideas for all of science we should unleash the potential for maximum abduction through interdisciplinarity, but at the cost of reducing longer-term differences between fields that serve as reservoirs for future abduction" (26).

Given this tradeoff, how might we assign a discount rate for innovation? Are there certain paradigm-shifting innovations, such as the "AI as Alien Intelligence" suggested in "The Paradox of Collective Certainty in Science," that might justify maximizing abduction in the present without regard for the "reservoirs for future abduction"?

[michelleschukin]

Discussion Question: The Role of Education in Balancing Innovation and Stability

The paper Destructive Creation, Creative Destruction, and the Paradox of Innovation Science explores how institutions designed to foster innovation can become resistant to change due to bureaucratic inertia and risk aversion. This raises questions about how education shapes our approach to innovation. As an economics major, I’ve noticed that many business school courses emphasize operational efficiency, risk minimization, and supply chain management—concepts that align with development rather than disruption. If innovation is often driven by creative destruction, should institutional education systems incorporate more emphasis on disruption and high-risk innovation? Does a traditional business education make individuals more risk-averse by prioritizing stability over experimentation? How can we design educational models that balance both developmental efficiency and disruptive creativity to cultivate future innovators?

[xdzhangg]

A key paradox in scientific innovation is that as colaboration increases, its scientific validity rises while replication falls. This is because as scientists trust each other, there is a feedback loop reinforcing shared data, beliefs, and methods. Scientists then become increasingly homogenous in their approaches and genuine innovation becomes difficult.

Question: how can AI enable the democratization of data, methods, and frameworks without institutionalizing early success and thus reduce innovation? For example, could AI provide alternate empirical / theoretical pathways along side in each tried-and-true approach or dataset? Perhaps it can suggest other ways to verify and replicate the same result such that innovators are not hindered by the established success of their predecessors.

[joezxyz]

It is interesting to consider the idea of replicating experiments and the possible epistemic bubbles that doing so creates. In terms of the ideas of research and the context in which scientists become closer and thus more trusting of each other's methods, I wanted to chase that concept back into the development of AI models.

If we are to consider AI as something eventually aimed to have its own autonomy and be able to further enhance innovation, wouldn't this sane issue affect the AI? It is inputted with an incredible amount of data, but if the developers behind the data are people similar to R&D workers who have formed their epistemic bubbles and have already experienced the trade-off of validity vs replication, isn't the outcome going to be the same? How can such an outcome be avoided?

[ggracelu]

In “Destructive creation, creative destruction, and the paradox of innovation science,” Cao, Chen, and Evans argue that innovation studies could become “more powerful,descriptive and predictive” if they viewed “social, material and cultural forces as co-constitutive and co-evolving” and recognized “the influence of technological innovations on society and culture.” In other words, there is a two-way influence between innovation and social, material, and cultural forces.

How can we empirically measure the social and cultural impacts of innovation? How can we craft policy to incentivize socially beneficial innovation, not just all innovation across the board? How can we anticipate the social and cultural benefit/harm of innovation externalities before they occur?

[Hansamemiya]

Both the paper Meta-Research: Centralized Scientific Communities Are Less Likely to Generate Replicable Results and The Paradox of Collective Certainty in Science suggest that when scientists work closely together, they develop greater trust in each other’s work, but their findings become less independent and less likely to replicate. The study on drug-gene interactions provides empirical support for this idea, showing that centralized research communities produce less replicable findings than decentralized ones.

Why does this happen, and what mechanisms drive this effect? Is it because researchers become too confident in familiar methods, or because using the same data and techniques makes it harder to test ideas independently? Does this issue affect some fields more than others, particularly in areas where replication is expensive or difficult, such as pharmaceutical research?

[siqi2001]

The Sustainability of Epistemic Diversity: But What If We Are Speaking to the Power?

As a humanist stepping into an Econ class from an outsider’s perspective, I was fascinated by Eamon Duede’s paper “The Social Abduction of Science.” First, the paper provides a critical insight: The paradox of abduction, namely the difficulty of simultaneously being an insider who discerns the anomalies and an outsider who works to resolve the conflicts, can be dismantled by understanding abduction as a social process. Second, it asks a pressing question: Given that social abduction relies on both the sustained separation between disciplines to preserve diversity and points of contact to facilitate abduction, how should we manage the relationship between disciplines? This question is echoed by “The Paradox of Collective Certainty in Science,” which suggests that while communication across fields potentially addresses the replicability challenges, it leads to “competitive dynamics in the economy of attention” that ultimately renders diversity unsustainable.

In response to Duede’s question, I wonder if our hesitation to promote communication could be alleviated if we seriously consider the dominant preservation power as our reality. Surely, it would be a scientific disaster if all scientists were merged into one field, focusing on similar topics. However, for social, structural, ontological, and even financial reasons, we know that insiders always have good reasons to defend their fields and resist the outsiders. Just like how we want to support new entrants because we know incumbents always have resources and incentives to defend their position, can we support interdisciplinary communication with less reservation if we know the resisting power is always strong?

[henrysuchi]

The application of Feyerabend's notion of counterinduction to the more practical question of how scientific practices ought to be arranged was very interestingly posed by Evans and Duede (2021). The fact that scientific, and even epistemological, advancements come from people willing to defy the status quo poses a problem, but also potentially a path forward for science. The university system and the form of journals and the like tend to favor incumbents. Evans and Duede (2024) also note the potential for "antitrust" issues in science, where large groups tend to swallow up the potential for true scientific advancements. What does a potential reform look like that would encourage disruption in science? Is there a way to encourage scientists to form more "flat" or just smaller teams? And how can this goal be achieved while maintaining the financial viability of the academy?

[dannymendoza1]

The paper on Destructive creation, creative destruction, and the paradox of innovation science discusses how disorder and disruptions are pre-conditions for innovation. It presents traumatic shocks such as wars and natural disasters as one mechanism for empowering innovation at a societal scale. Specifically, it states that “by breaking apart complex institutions, these shocks facilitate the process of creative recombination by dislodging parts of nature, culture and society previously unexposed and unavailable for recombination” (Cao, Chen, Evans 4). My question becomes, is the innovation that results from these traumatic and devastating events worth the destruction and loss that also comes with such events? In other words, are events such as war and natural disasters necessary in order to continuously push the technological and innovative frontier line forward? After a certain point of continuous destruction, repairing, and improvement, can we ever reach a point in which our society is immune to shocks?

[yasminlee]

The Paradox of Collective Certainty in Science emphasizes the importance of replication for epistemic robustness, however, Abduction and the Logic of Scientific Advance focuses on how abductive leaps can shift paradigms. My question is, how should the government/institutions balance funding between research that replicates past work but also seeks entirely new directions? The paper on collective certainty tells us how scientific certainty is often an illusion, and the other paper tells us how major progress can come from embracing uncertainty, so does that mean we should focus significantly more on the provisional nature of scientific knowledge?

[carrieboone]

Certainly! Here's a class question that aligns with the themes of abduction, scientific progress, and the paradoxes of innovation:

Question:
Using abductive reasoning, it would be rational to assume that this question was generated by ChatGPT and that I accidentally forgot to take out the prompt. But if I had written this post three years ago, nobody would assume that. We now reason whether online users are bots, or videos are AI-generated, even though not long ago, technology this advanced didn’t exist. Our abductive reasoning adapted to the development of AI. As described in “Abduction and the Logic of Scientific Advance”, our reasoning evolves and adapts to environmental changes, including the advance of AI reasoning. As AI reasoning advances, how else will our own reasoning change in response? If an AI attempted perfect human reasoning (not better, but human), would it ever be indistinguishable, or would there always be a cycle of AI trying to “catch up” to human reasoning, which has already adapted to the AI trying to “catch up”?

[druusun]

The readings explore how knowledge accumulation and diffusion drive technological progress but also reveal structural inefficiencies that slow innovation. One paper highlights the importance of "bottleneck technologies," where breakthroughs are constrained by missing complementary advancements, while the other discusses the role of research spillovers in determining long-term productivity growth. Given that firms and governments allocate R&D funding based on anticipated returns, how can policymakers design incentives that encourage investment in foundational but slow-maturing technologies without stifling short-term economic growth? Additionally, how can we measure whether an economy’s innovation ecosystem is too focused on exploitative research rather than exploratory breakthroughs?

[salhurasen]

The “Destructive creation, creative destruction, and the paradox of innovation science” paper introduces the concept of destructive creation. It views destructive creation as the process in which existing systems and orders are destroyed and acts as a precedent to innovation and creative destruction.

The paper presents the notion that on a societal level, disruptions such as wars and crises can facilitate innovations. Not only that, but also economic crises can facilitate innovations. Even though this is true, more often than not disruptions on a societal level do not result in innovations and usually lead to severe instability and conditions that hinder economic growth. What are the prerequisite conditions on a societal level for such disruptions to act as a precursor to innovation?

[cmcoen1]

We've learned that throughout scientific history there always seems to be some push and pull between philosophers, who focus on the logical foundation of scientific ideas, and sociologists, who see knowledge as shaped by social factors. How does the idea of a ‘social syllogism’ in abductive reasoning help bridge these two perspectives? And what does this tell us about how we decide whether big discoveries, especially those that cross multiple fields, are really valid or trustworthy?

[rzshea21]

As scientific endeavors become more advanced, requiring more expertise, human capital, financial capital, etc., this increased specialization will result in high expertise for different institutions, departments, and fields of study, but risks entrenching these fields in what the authors of this week's readings called intellectual silos. This entrenchment may continue to worsen the replication issues we see in the modern scientific community, particularly as a push for standardization increasingly favors replication and confirmation over novelty and innovation. Duede and Evans argued that this innovation comes disproportionately from interactions between diverse institutional knowledge groups through social abduction. Specifically, insiders or experts within their scientific domain can effectively identify anomalies in data but may be entrenched in standardized models that aren't able to account for them, while outsiders can provide novel explanations based on their own diverging methodologies and expertise from a separate domain. My question here is how can we surpass institutional barriers to scientific collaboration and progress, creating an effective balance between expertise and intellectual diversity that optimizes scientific progress and maintains some useful standardization and reproducibility? How do we convincingly (socially and financially) incentivize funds away from intellectual silos and promote less structured (highly collaborative), high risk, but highly innovative research?

[yhaozhen]

I’m struck by how the authors frame abduction as a social rather than purely individual phenomenon. This opens intriguing questions about the role of institutional structures that may constrain or enable cross-field “conversations.” For instance, do high-stakes grant panels actually undermine the creative collisions needed for abductive breakthroughs? I also wonder if collaboration tools (like Slack or open-source platforms) might cultivate or dampen serendipity between “insiders” and “outsiders.” Another point that nags me is whether protecting disciplinary boundaries risks inadvertently blocking outsiders in practice. Could a more fluid system still preserve enough “difference” to foster these surprising pairings? Finally, I’d love to see more on how power dynamics—who gets heard versus dismissed—affect the trajectory of anomaly recognition and resolution.

[spicyrainbow]

In class, we discussed how innovations that did not evolve from existing technologies often fail to have creative disruption due to their incompatibility with the broader technological structure and ecosystem However, the article "Destructive creation, creative destruction, and the paradox of innovation science" highlights that innovators often access new ideas through distant connections and that true innovation frequently emerges from disruption and disconnect, thriving in environments where existing systems have broken down.

My question is: If disruptive innovations are more likely to emerge from such disconnections, but are also more likely to fail due to their initial incompatibility with existing technologies, what policies or support systems could help these radical innovations survive the early stages of adoption and have a chance to find a way to integrate and connect with existing technological ecosystems? How can we create environments that both encourage disruptive creation and creative disruption thats stems from incremental evolution of existing technology?