03/31: James Evans

[ehuppert]

Comment below with a well-developed question or comment about the reading for this week's workshop. These are individual questions and comments.

Please post your question by Wednesday 11:59 PM, and upvote at least three of your peers' comments on Thursday prior to the workshop. You need to use 'thumbs-up' for your reactions to count towards 'top comments,' but you can use other emojis on top of the thumbs up.

[sabinahartnett]

In Flat Teams Drive Scientific Innovation you analyze the impact of L-ratio on scientific innovation. I'm wondering how you controlled for scientists' career experience, education level, citation frequency / other indicators of their potential to contribute? I suppose I see a bit of a chicken and egg issue here - that it is the potential of the contributors to contribute in a meaningful way rather than the structure that causes this correlation. It appears to still be a possibility that Flat teams are perhaps composed of more equally qualified (/contributing) authors and thus appear more egalitarian. How do you see this / address this in the research?

[pranathiiyer]

Adding on to Sabina's comment, the paper on Flat Teams Drive Scientific Innovation really makes me think about its broader implications on collaborations in research, especially for young researchers starting out. How do young researchers produce novel ideas or research when working with more experienced researchers is such an integral part of collaboration and understanding the research space? Perhaps the idea is not to understand hierarchy by experience but contribution, yet given that research design, problem identification skills, and other "non muscle, brain, and fat" tasks could be a virtue of experience, how do you think young researchers or researchers who are primarily "support" authors in hierarchical teams can work towards producing novelty?

[chuqingzhao]

Thank you for sharing the work! Amazing papers on team science and science of science. The findings from the paper "Flat Teams Drive Scientific Innovation" echo with a wide range of management theories and experiments of how decentralized teams and self-managing organizations are more creative.

1. Thinking of dividing the team structure into a binary classification, I wonder is there possible to consider the co-existence of flat team structure and hierarchical structure in scientific production. Big science labs usually divide research projects into several small and agile teams; and pioneering works are delegated to small teams. At big science labs that encourage idea exchanging and gather more resources, small research teams would be more likely to receive feedbacks from diverse audiences and put lab resources to use. Broadly speaking, I wonder is there any confounding variable that might also drive the scientific innovation such as knowledge diversity of team, information coordination, team culture, etc.?

2. I am personally curious about the dynamic of the flattening process across organization's lifecycle. Because it seems unclear that even though flattening become a trend over past decades, we can still observe conflicts occur when large companies acquire small silicon-valley- styled startups. How should organizations find a balance overtime? For different types of organizations (such as startups vs government agencies), how should they design team structure?

[JoeHelbing]

Some simple napkin math, avg life expectancy from 1960-2018 in US increased 14.3% (69.9 yrs to 78.9 yrs) but the percentage of "Old Scientists" looks to have more than doubled, tripled, or quadrupled in most fields, with all the attendant frictions related to this phenomenon in your paper "Aging Scientists and Slowed Advance". Granted that "Old Scientist" is defined on the absolute scale of 20 years, but that small relative increase in life expectancy versus the large relative increases in age composition for the various fields would imply there's some structural factors that are exacerbating the trend. Curious if you would speculate on that.

Secondarily, the paper brings to mind the heuristics of credentialism and if the the increasing age of various scientific fields are possibly reinforcing that issue as well.

[Hai1218]

My questions and reactions are most concerned with Flat Teams Drive Scientific Innovation.

First, I think the result is presented to me quite intuitively - flatter organizations with less top-down hierarchical structure while" taller" and more top-down structures tend to produce a more focused result that centers around the will of those who spearheaded the research.

I must ask that, apart from the fuzziness of operationalizing "novelty," how would you address the fact that "developing" organizations tend to be more egalitarian while "established" ones are more hierarchical almost all the time; and that "developing" teams tend to create novel results but the other tends to build on existing results?

Let me elaborate: Teams still in the making or are still "brainstorming" tend to be more egalitarian. They are searching for something innovative or still in its infancy - and if they succeed, they tend to create something "novel." On the other hand, teams with a robust organizational goal, a clear objective, and established methods tend to be organizationally rigid and usually have a top-down approach to things - it is not surprising that they tend to build on existing ideas.

Does the paper reaffirm these suppositions/assumptions? If these suppositions/assumptions are accurate, would they render this paper's central motivation and finding moot?

[ValAlvernUChic]

For the paper Aging Scientists and Slowed Advance, the processes described were attributed to individual-level behaviors and I was wondering to what extent you think that we should consider institutional effects on the individual decisions of these scientists. I'm not completely familiar with the academic environment but the pessimist in me would find it hard to believe there are no bureaucratic processes that influence older scientists' decisions as well (also assuming the longer they are in that institution, the more entrenched they'd be in its bureaucracy). This is important (at least to me) cause it'd help us get to the mechanisms of these - whether psychological or organizationally driven.

[Lynx-jr]

Recently my Booth class has this interesting case of the apparel selling site Zappos which incorporated an "extremely flattened" structure (before 2010) and the reading was signaling that employees experience confusion since nobody was there to lead them. Although I couldn't immediately think about data/papers that contain a company's L-score-related info, it would be interesting to dig further in that direction. Moreover, I cannot find the discipline-related remarks in the paper. I think there should be discipline-specific characteristics existing -- manual-experiment-based disciplines (such as chem or bio) might have a greater level of (unavoidable) hierarchy while social sciences and cs tend to be flatter.

[erweinstein]

My questions/comments are about Aging Scientists and Slowed Advance.

1. I always love to see fun (and useful) new applications of Hierarchical Linear Models! In the model for Reference Age (Table S2), what should we think about the non-statistical-significance of gamma_101 and gamma_102? (If I'm interpreting it correctly, the Mean Age of a field and the Mean Team size of a field don't meaningful effect the strength of the effect (the slope) that scientist Age has on Reference Age--an effect which is statistically significant.) I agree that it doesn't undermine your results and interpretation, but should we be surprised that (to slightly oversimplify) the slope of the relationship between scientist Age and Reference Age is more consistent than we might expect (even as the mean/intercept differs, e.g., across fields; and it varies in a few other ways you note, just not reliably with field's Mean Team size and field's Mean Age)?

2. Your identification and comparison of "early-bloomers" and "late-bloomers" among the scientists in your sample was very interesting, and it seems like that could be its own paper! The pattern you found, that the overall effect of scientist Age on citation behavior holds for both types, but '“early-bloomers” experience a faster increase in reference age', is consistent with something David Galenson (the labor economist/economic historian some of us might know since he's here at UChicago, although I think he's been on leave recently) hypothesized at least 12 years ago. He has studied the differing career life-cycles of "early-bloomers"/"conceptualists" and "late-bloomers"/"experimentalists" in the arts (poetry, literature, painting, film, music, etc.) and, citing Einstein and Darwin as exemplars, argued that the same pattern ought to hold for scientific innovation, but as of the time he wrote about it--I have an NBER working paper from March 2010--he noted there had so far been little systematic or quantitative study of these important issues that focused on scientists. You and your coauthors (and colleagues at the Knowledge Lab) have made so much progress in these areas since then!

[ShiyangLai]

Thank you for sharing these papers. My main question is how should we identify innovative scientific works. Are papers with new keywords more innovative than papers without new keywords? In my view, scientists are always trying to make their works seems innovative in different ways. Some of them may try to use different wording in their papers to make their works seemingly creative but actually are not. Second, are works using common keywords not innovative? In a social science study, if it employed a method in another field, though the keyword for that method already existed, I still think the study is innovative somehow. Further, I think scientific innovation is not a yes or no question, it is better to treat innovativity as a spectrum. How to measure a paper's innovative level is then another profound topic.

[TwoCentimetre]

In the paper Aging Scientists and Slowed Advance, it says that those who move to a new place are more likely to be open to new ideas. I wonder if there is a causal relation. It appears to me that moving to a new institute is already a signal showing that this person is open to new research ideas because this person is willing to cooperate with new researchers. Another point is that after moving to a new academic institute I suppose there should be some requirement for those newcomers to do some kind of research. Do you think that could be the potential reason for this phenomenon? As for the research design, it contains 244359707 authors with 240874887 articles which is roughly one article per author. So, it seems that this data is more like a time series. Then does the pattern found in this paper merely come from the change of time?

[Raychanan]

I am particularly intrigued by the first paper on aging scientists. They provide intuitive results. In certain ways, the tendency of those aged students may be explained by the way our human brain functions. However, I am curious as to how we might interpret the contrasting landscapes in different areas (especially between computer science and social science)?

[taizeyu]

Thank you very much for making a presentation for us. The question I want to ask is about the article "Flat Teams Drive Scientific Innovation". First of all, I saw you wrote that you choose the data from four journals, including PNAS, Nature, Science, and PLOS ONE. It is sure that these four journals have good information, especially Nature and Science. However, it does not mean that other journals are not good. For example, there are some good journals in different specific areas. Therefore, I want to know the reason you choose these data. Secondly, this paper get the conclusion that flat teams can get more innovation. However, why there are still some company not using the flat teams. Whether most groups should use this model when they are doing work. In other words, does tall, hierarchical teams also have some advantages which make some company still use this pattern?

[luckycindyyx]

Thank you for sharing such interesting paper with us. I especially like the paper Aging Scientists and Slowed Advance. I am curious about the potential mechanisms and explanations on why aging scientists and fields cite older literature. Is that because they are more familiar with the older papers, which might published when they were still students? Or is that possible if the citation could also be correlated with their academic connection (e.g. citing paper of who they have known for years)? And if there are certain stories that you and your coauthor believe, how could you verify those hypotheses? Thank you!

[Hongkai040]

I am really interested in the topics of these two papers. Can we use the research design of paper 1 (about hierarchy of teams) to validate the results of paper 2 (aging of scientists)? In many cases that young scientists collaborate with older scientists, the role of the young scientists and older scientists are different. I think maybe it's worthwhile of exploring the collaboration pattern of older and young scientists.

I have another question about using the age of cited articles as an indicator in paper 2. Most papers weren't highly citied in the history of science as they're not very innovative. Does this truth have influence on the results of this part? I can come up one possible explanation that young scientists may be prone to cite recently published papers as those papers are temporarily remembered while older scientists are more capable of tracking the history of the field and finding old highly cited papers.

[hhx2207061197]

Hi Professor Evans, Thanks for the sharing. The question I want to ask is about the article "Flat Teams Drive Scientific Innovation". I see you have mentioned that you select data from four journals, which are all quite informative. However, it does not mean that other journals are bad. So I would like to know why you chose these data. Thanks.

[xin2006]

Thank you for sharing such amazing work! I am particular interested in the paper Aging Scientists and Slowed Advance, and the following are my questions. First, I am a little confused about the methods of mapping scientific fields of paper and quantifying keywords repetition. From my point, most scholars are focused on certain field and keep going deeper and deeper, that is, they could come up with new ideas within the domain, keeping the general topic and keyword similar. So, I wonder to what degree can the rate of changing topic and keyword coverage serve as good measurement? In addition, since you have not identified the casual mechanism of aging being likely to slowdown in the churn of ideas, I am just curious if you consider to figure out the reasons in the future, by survey, experiment, etc.? Because the finding makes sense but a little surprising, and the reasons are definitely general and meaningful considering the circumstance is common for all the fields and periods.

[MengChenC]

Hi James, thank you for another two pieces of interesting paper. In the Flat Teams Drive Scientific Innovation, the paper discovered that "relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas; increase productivity for those on top and decrease it for those beneath; increase short-term citations but decrease long-term influence." My main question is what would be the reasons causing this finding and phenomenon, and would this finding be generalized to other team formats as well like in a business setting or government structure? Thank you.

[FranciscoRomaldoMendes]

Why is having newer keywords necessarily considered more innovative ? Most research in Computational complexity uses the same notation from over 50 years ago. In the same vein, why is more disruption necessarily a good thing? Generally fields tend to be highly disruptive when they're young, gradually settling into a steady stream of intellectual thought. While it's certainly true that senior scientists are more likely to defend ideas than attack them, in many ways this is the nature of academia, confirmed by the fact that when younger disruptive scientists reach a certain academic age they tend to defend their disruptive ideas which are at that point norm de rigeur (as opposed to continuously coming up with disruptive ideas). By which I mean there is nothing special about such a phenomenon. Finally, there's a certain self selection bias at work here, disruptive papers are a priori less likely to make it to publication, it's much harder to convince editors that something disruptive is valuable unless it's really really good.

[a-bosko]

Hi Professor Evans,

Thank you for sharing your work with us! Your study on team structure is very interesting and relevant to us. It is interesting to me that flat teams are more likely to produce more novel ideas as compared to hierarchical teams.

I was wondering if you believe that these findings apply to groups across all cultures, and groups with people from various cultures? Looking at individualistic versus collectivistic cultures, one could believe that the benefits may vary. The same goes for people across different ages and genders. My main question is about the generalizability of these findings.

Thank you for your time and your insight!

[Dxu1]

Thank you for sharing your papers. I am interested in the interaction between L ratio and the size of groups in your article ""Flat Teams Drive Scientific Innovation". You argued that when L ratio is higher, the research contribution tends to bring more novelty to the field. Smaller team also tend to have higher L ratio (this could be due to either more engaging interaction or simply by construct). What are the correlation between novelty of contribution and size of teams? How do the three factors interact?

[awaidyasin]

Hi Prof.,

I very much enjoyed your 'Aging Scientists and Slowed Advance' reading. One might intuitively believe many of the conclusions presented in your research, but seeing evidence is reaffirming.

I was wondering how much of this phenomenon could instead be explained by the existence of different schools of thought in a field? In social sciences (macroeconomics, for example), one might be able to discern visible patterns in one's research/conclusions. Is it possible that one's school-specific philosophy (instead of physical or intellectual aging) might be driving these results? Someone trained in a particular tradition would, on average, want to stay within that tradition and is more likely to criticize work from an alternative tradition? (One way to explain why scientists get critical over time might be simply that early on in their career, the focus is on establishing their name in their tradition rather than critiquing works from other viewpoints. As one gets established, they move their attention to other methods. In this case, the causal factor is not age (though age is definitely correlated with the argument above.)

[Yaweili19]

For the paper Aging Scientists and Slowed Advance:

I really liked the flipped-over structure of this article: summaries, findings, and discussions first, then long reports of methods and results. How much would you recommend we use a similar structure for our papers? How well is it accepted among peer-reviewed journals?

[zoeyjiao1104]

Thank you so much for sharing your paper! The findings about aging scientists are very interesting and eye-opening. As you mentioned one of the limitations of the paper is the measure of average age within fields since science occurs within teams instead of individuals. I was wondering that instead of averaging the age of scientists, could we take a different approach to measure the age gap within teams (the gap between the oldest scientist and the youngest one) and see if the gap would increase across time. In addition, I was curious if the findings universal across culture and gender.

[nijingwen]

hi, Professor Evans, Thank you for sharing your idea for flat management. When I was intern in Bytedance last summer. This company is using flat management. There are many advantages like: people are easier to provide creative ideas, people are feel more respected. However, there were also some disadvantages like: there is no person to connect and coordinate with each other. Hence, the goal for the team may not be consistent, which will lead to inefficiency of the team work. To balance the advantages and disadvantages of flat management, what improvement the companies can do??

[sdbaier]

In Flat Teams Drive Scientific Innovation is there not a “hidden variable” of research assistants and (non-coauthor) peer researcher providing guidance on earlier manuscripts? Research assistants would count as direct support, peer researchers as indirect support – both influencing the trajectory and shape of the paper, but (more often than not) neither being explicitly mentioned in the self-reports, or at least not under section author contributions. Was this an issue when cleaning and analyzing the data?

In Aging Scientists and Slowed Advance, one of the findings is that aging scientists are more likely to criticize young scientist’s articles. Is this because of the novelty of researchers themselves (limited social capital, limited recognition, limited track record) or of their ideas (potentially contrasting with extant research of older scientists)?

More generally, is the finding that as scientists age, they tend to work on familiar topics negatively connotated? Arguably, their increased commitment to familiar topics allows to solidify the foundations of a topic and corroborate prior (disruptive) findings in subsequent studies – eventually allowing a next wave of scientists to critique (and hopefully improve) the status quo.

[yutaili]

Thanks for sharing your work. I want to ask a few questions about the Flat Team Drives Scientific Innovation paper.

1. For the method part, it seems like you did content analysis of the author contribution data by extracting the key words from authors's statement. I wonder how strong the correlation is when you match certain verbs to certain team roles (coordinate -> lead, develop -> direct support, discuss -> indirect support)?
2. Do you count the number of disciplines covered in the sample? Is it possible that some discipline may share a different pattern than the finding suggests?
3. With respect to the findings of this paper, it seems like you are suggesting a higher L-ration (flat team) is more favorable in generating novel ideas and make long-term impact. My question would be could this finding be generalized to all scientific inquiry? What if the goal of a research project is to consolidate/generalize an old idea. Would like to hear your thoughts on this.

[isaduan]

On Flat Teams Drive Scientific Innovation, I am a little bit confused of why "interpret" counts as leadership role whereas "analyze" counts as direct support role. Maybe I have missed this when reading the paper, but is there any validation that the clustering makes sense such that research activities can be categorized into the two roles?

[hazelchc]

Thank you for sharing two interesting pieces of work with us! I have a question about the first paper. I am wondering if the results can be found in other creative industries such as design and film industries? Apart from innovation, I think other factors such as efficiency are also interesting to study.

[Toushirow1]

Hi Professor Evans,

Thank you for sharing your research with us. I found the article "Flat Teams Drive Scientific Innovation" very interesting. A lot of startup companies use a flat team structure, although I'm not sure if their purpose is to drive innovation. I wonder if the relationship between a flat team structure and innovation is affected by team size. What team size better reflects the relationships found in this article?

[borlasekn]

Thanks for sharing such interesting work, that will impact us as we explore doing our own research! In the article "Aging Scientists and Slowed Advance", you find that aging scientists critique young scientists ideas. This stubbornness of aging scientists seems like it could be rooted in learned scientific proof. Often, these scientific proofs were established by a certain demographic of white male scientists. To what extent should these notions be examined together (i.e. that accepting scientific "fact" re-establishes institutions in place, and perhaps the stubbornness of aging scientists is in conjunction with these institutions, especially in the US?)?

[kuitaiw]

Hi Prof Evans， Thank you for sharing your work. My question is about “Flat Teams Drive Scientific Innovation”. First of all, I think the conclusion of the article is obviously correct in real life. However, I find that the reality is that when a team is growing up, the team will obviously be relatively equal and more creative. But when the team gained a certain advantage, the structure of the team began to involuntarily become rigid and have a hierarchical culture. So I want to know what solution can be used to avoid this more common situation in reality?

[zhiyun0707]

Hi Professor Evans,

I find it really fascinating to read your paper on the Aging Scientists and Slowed Advance. I have a question on the section How Aging Scientists Stay Young, you mentioned that scientists and scholars who "collaborate with younger coauthors or move to new institutions are more likely to cite references. " I am wondering that are there any other internal factors such as the researchers personal traits (some people prefer to try new things while others want to stay in their comfort zone) that leads to some scientists to cite more new reference? Thank you!

[hshi420]

For Flat Teams Drive Scientific Innovation, I was wondering about the possibility of third variables' effect on generating novelt results. Especially the effect of size of the team, the diversity of the team members (difference in erudition, experience, culture). I'd like to see ML models classifying high/low possibility of generating novel results, and investigate feature importance of the models.

[sushanz]

Hello Prof Evans, thank you for sharing! On your paper Aging Scientists and Slowed Advance, I wonder how you and other researchers consider confounding factors such as team contributions. Especially how did you consider these factors while making OLS regressions or mapping the scientific fields of papers? I noticed you include an attribute MeanTS as the team size, and wonder how did you take into account of their team members' ages? Is it possible to have some correlations here?

[Sirius2713]

For Flat Teams Drive Scientific Innovation, is it possible that the effect of hierarchy/L-ratio is associated with country or cultural environment? For example, in some countries, it's the social norm to have a tall team. While in some countries, flat teams become more prevalent.

[AlexPrizzy]

The findings of these papers are intuitively unsurprising, such as egalitarian teams having distributed and greater long term impact, as well as fields such as mathematics which rely on long standing theories having older citations on average. Though the idea of an academic "fountain of youth" by encouraging aging scientists to explore new fields and collaborate with younger peers contradicts the stereotype of older scientists seeing younger scientists as research support minions.

Given this notion, would the age of a field impact the anti-aging properties of scientific exploration? For example, if an aging scientist were to cut their career short by exploring other fields, would the rate of aging be the same if they were to branch out into a younger field such as computer science which is more permeable to new pioneering ideas versus an older field such as mathematics?

[k-partha]

Thanks for presenting, Prof Evans. As many others have pointed out, multiple relevant factors to innovation may be correlated with 'flatness'. Which of these do you see as the most interesting and worth exploring further?

[LynetteDang]

Hi Professor Evans, thank you so much for sharing your papers! For Flat Teams Drive Scientific Innovation, I am wondering if you have ever thought about looking at publications in industry research in addition to academic research and cross compare them. Do you think the pattern in industry research will match up with academic research? Why or why not?

[atowey-uchi]

Hi Professor Evans! In Flat Teams Drive Scientific Innovation, I am wondering if you were able to ascertain any cultural differences by country or continent, or were all of the samples taken from the US? I ask because I find the words selected as denoting "leadership" interesting. Because describing one's work is inherently subjective (and the paper relies on self-reports), I am curious if these descriptions differ by country (or language-translation, if applicable).

[ttsujikawa]

Hi Prof. Evans, thank you so much for presenting your work. When I read Flat Teams Drive Scientific Innovation, I found it interesting how you reveals causal relation between network structure in team and innovative ideas. As some student mentioned above, I was wondering how diversity in culture, gender, and as such could possibly affect innovative ideas in project team? Or its diversity modifies structure of the team? Reason behind this question is that it is often said that, in business world, diversity plays important role in creating innovative ideas. I just want to hear how this applies to research field.

[xzmerry]

Thank you for sharing. My questions are as follows:

1. I am thinking about the application of these research results in mechanism design. Because both L-ratio and the problem of aging scientists are related to the specific structure of an organization and the institutional design. What would be the related implication of your research results with regards to the design of organization structure? And what would be the impeding institutional factors?
2. Besides, how do you think of the problem of scaling here? Currently, your research is based on scientific research team and the size of which is of limited variation. Would there be heterogeneous effects in your research if we scale up and down the team? If so, how should we interpret the result under different scales?

Thank you.

[XinSu6]

Professor Evans, Thank you so much for sharing all these work! The Aging Scientist and Slowed Advance paper is the one that interests me the most.

My question are as following: First, the paper brought up some very fascinating phenomenon like the aging scientists are more likely to criticize younger scientist’s work. And they are more likely to open to new ideas when they move to new places. What can be possible reasons for these? Are there any positive or negative effects? Also, since some of the processes described were attributed to individual level behaviors, can there be any bias because of that? Finally, the flipped-over structure used here is indeed impressive and insightful. Do you think it can be used anywhere else like on another topic or even another field?

Thank you and looking forward to the talk tomorrow.

[y8script]

Thank you for sharing these interesting papers! I have a question about the interpretation of the L-ratio in Flat Team Drive Scientific Innovation. As a higher L-ratio stands for more members making a leadership contribution, is it possible that the average engagement of individuals actually drives the differences in research novelty? For example, some authors may be 'hitchhiking' in a study without putting a lot of personal effort into it. As a result, their minimal engagement may make the research less innovative compared to those researches where most authors highly engage in the research. Are you accounting for this factor in the analysis?

[fyzh-git]

Thank you Professor Evans for bringing us the topic on flat team and innovations, which I've myself also had a certain experiences when interned at different institutes of different levels of "flatness". One thing I think important but a bit different from your definition of "flat team" using lead-ratio is the exposure rate of someone in a team's opportunity to directly report their progress to the highest director of whole teams, since this measures in a more direct way on how closely connected between all levels of the members in a team -- just using the "distance" between the "top" and the "bottom" of a team. This method is more applied to the research department in the industry though. Would you like to compare and contrast this method with yours? Look forward to your presentation on Thursday!

[Yutong0828]

Thank you very much Professor Evans! It is really refreshing to learn more about the scholars in the academia while I might become one of them in the future. I have several questions for the two papers:

• Xu et al., 2022

  • What are the exact form of the self-report measurement? Was such information extracted from the paper itself or by surveys?
  • What is the relationship between L-ratio and direct/indirect supporters? Does the proportion of direct/indirect supporters als omatter?

• Cui et al., 2022

  • Is there any meaningful heterogeneity in terms of the aging effect in different fields? For example, I have noticed that social science researchers are less likely to contrast young scientists’ claims.
  • Another concern is how can we distinguish between settling down in a specific area with being resistant to new ideas. I mean, we sometimes think it necessary for a researcher to go further and explore in-depth throughout their career path.

Many thanks! Looking forward to your presentation tomorrow!

[DehongUChi]

Thank you for sharing your work with us Prof. Evans, my question is: what do you see as the best remedy or solution to the current problems of the aging scientists and decline in new disruptions in academia? Do you see this problem caused by the emergence of many new fields of study that dilute the talents, or the system of academia itself?

[yhchou0904]

Hi Professor Evans! I am especially interested in the article "The Aging Scientist and Slowed Advance." The paper said that aging scientists tend to stay on familiar topics, cite older articles, and critique new ideas, and early success would accelerate the aging process. I am just wondering if there are other ways to define these kinds of achievements and if there are, what is the pattern of scholar's academic research? For example, the paper use citation as a way to distinguish whether a scholar is an early-success or late-boomers. I am curious about what it would be like if we especially take a look at scholars' research after they are granted tenure, would it be a different story?

[Qlei23]

Thank you Professor Evans for sharing you latest research! It's interesting to see the dynamics of scholars' contribution and stream of ideas changing as they age in the academia. It is mentioned in the paper that aging scientists are more likely to criticize young scientists articles. I'm wondering whether the fact that academia is aging has some negative externality on younger scientists. For example, the age when scholars publish their first paper may increase as the academia gets more competitive. Looking forward to your presentation tomorrow!

[NaiyuJ]

I'm considering if there're any confounders in this puzzle. Does low novelty necessarily result from the high hierarchy? A high hierarchy may imply that there is less "total effort" involved in the project. Other than that, I don't think developing existing ideas is not as meaningful as producing new ideas. Both two can advance science in different ways. We may not want to make a judgment in that sense?

[ZHE-ZHANG-0213]

Thank you, Professor Evans, for your introduction. As many others have pointed out, several related factors associated with innovation can be related to "flatness." As you say in your article, older scientists are more likely to criticize the work of younger scientists. And they are more likely to be open to new ideas when they move to new places. What can be the possible reasons for these? Can your research results be applied to the design of organizational structures? Do you have any suggestions?