ecoevogames

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In the absence of government leadership, I commit to be carbon neutral by 2030. Will you join me?

This is a post about climate change and my personal pledge to go carbon neutral by 2030. To start, let me be clear: I’m not here to debate, or convince you of the science of climate change. Go ahead and stop reading now if you want, otherwise let’s continue. This will be a long post, because I want to be thorough. 

Governments have been talking for decadesabout the growing problem with global…

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Books and Coffee, and the Sixth Extinction. Part 3: Extinction rates.

Books and Coffee, and the Sixth Extinction. Part 3: Extinction rates.

I recently gave a public lecture in Purdue’s English department on Elizebeth Kolbert’s Sixth Extinction. (It’s a great book, go read it!) Because I read a number of interesting papers that I wouldn’t normally have read, I feel the need to do something with all the thinking I did! The first post was about the loss of taxonomic families, and the second post was about the loss of species during the…

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Books and Coffee, and the Sixth Extinction. Part 2: Extinction of Species.

Books and Coffee, and the Sixth Extinction. Part 2: Extinction of Species.

I recently gave a public lecture in Purdue’s English department on Elizebeth Kolbert’s Sixth Extinction. (It��s a great book, go read it!) Because I read a number of interesting papers that I wouldn’t normally have read, I feel the need to do something with that! The previous post was about the loss of taxonomic families during the “sixth extinction”, and whether we are anywhere close to losing as…

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Books and Coffee, and the Sixth Extinction. Part 1: Extinction of Taxonomic Families.

Books and Coffee, and the Sixth Extinction. Part 1: Extinction of Taxonomic Families.

Tonight I am giving a public lecture in Purdue’s English department on Elizebeth Kolbert’s Sixth Extinction. (It’s a great book, go read it!) All month as part of an event called Books and Coffee, people have been reading books on a dystopian and post apocalyptic theme. Three were fiction, and fairly bleak fiction at that. I sort of view my job tonight as answering the question: are we heading…

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How to make a Raspberry Pi touchscreen kiosk for your lab bulletin board

How to make a Raspberry Pi touchscreen kiosk for your lab bulletin board

There’s a bulletin board outside my lab. I thought it would be better if my Twitter feed could be posted. So I built a touch screen kiosk with raspi. pic.twitter.com/ObXshFyhdg — Gord McNickle (@EcoEvoGames) February 14, 2018 After I tweeted this, I thought I’d give some instructions for anyone that wanted to do the same thing.  This is a very easy project with RasPi, and is actually something…

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Overhead and indirect costs: what is the real cost of research?

(post by Gord McNickle)

I’m a political junkie. I view biology through a lens of game theory, so maybe it’s only natural that I would be interested in the game of politics. I tend to keep that to myself though, and my online presence mostly focuses on science.  Yet, often politics and science intersect, and granting public money to researchers to allow us to do our work is an important place of intersection.

I’m motivated to write on this topic based on this article in science magazine. The first sentence of that article by @jocelynkaiser explains it all quite nicely: “The Trump administration could slash $5.8 billion from the 2018 budget of the National Institutes of Health (NIH), yet still fund as least as much research by eliminating overhead payments to universities and research institutions, Secretary of Health and Human Services (HHS) Tom Price told lawmakers”. This strikes me as untrue because it misunderstands the true cost of research. I don’t do NIH research, but I imagine other agencies are next, and I’ve been surprised by the number of my colleagues that seem to agree with this sentiment. So, let’s think this through a little more carefully. Let’s consider some hypothetical research budget. What are the costs?

 A.      I probably need supplies. The physical things required to get the research done. There’s a cost.

B.      I probably need people to assist in carrying out the research. Graduate students, maybe a postdoc, probably some laboratory assistants. There’s another cost.

C.      Publication and conference fees. It’s good to present your work to the community, so let’s add some money there. This is usually a modest cost, but let’s add it in.

Total: $X

 Surely all I need is $X, which is my direct cost.  But most universities then require you to add indirect overhead costs that go on top of that. So if I need $X to do my research, and my university requires 50% indirect costs, I have to request $(X+0.5X). That extra $0.5X goes to the university. Surely I only need $X, and that extra $0.5X is waste that Trump can cut! Well, what do I get for that?

 1)      My department has a business office that maintains my various grant accounts. They also do a lot of my ordering and associated accounting. This includes the paperwork involved with making sure that appropriate taxes are handled since I work at a public institution. When I travel, I hand them my receipts and they do the paperwork on that.  When I bring in colleagues for collaborations, they also handle the accounting for those travel costs. I couldn’t do research without these people. Believe me.

2)      When I order supplies, they come through a loading dock. Staff are there to receive my orders, make sure everything is as it should be, and then bring it to my lab. Also, when someone accidentally delivered 2500kg of soil to my office, there was a crew of people available to move it to the greenhouse storage area. I’m not sure what I would have done without those staff.

3)      I have a laboratory space. The room has internet, a phone, city water, distilled water on tap, compressed air on tap, and natural gas on tap. It also has electricity which powers several instruments, and also it’s nice to have lights and heat. I imagine these things cost somebody somewhere.

4)      The heat broke down in my lab this winter. It was pretty nice to have some folks come by and repair that. I imagine if something else breaks, they would be there pretty quick.  I wasn’t billed for that, but I imagine that those folks didn’t volunteer to fix my lab.

5)      I use a greenhouse. There is a greenhouse manager, who probably doesn’t work for free. Again, there is running city water, and distilled water as well as high pressure sodium lights to keep my experiments running. It is heated and cooled.

6)      Someone takes out the garbage in my laboratory, and cleans the floors. Again, probably not for free.

7)      And then, I imagine there is an office somewhere that oversees all of this.  Making sure the staff above get paid, have benefits, administers their sick leave, hires replacements if necessary. Scientists like to complain about administrators, but I don’t want that job.

 This is far from a complete list. But notice the indirect cost list is already longer than the direct cost list.  And I don’t have to think about those things. They just get done. And paying for these things is the true cost of research.  

So if the Trump administration stops allowing for indirect costs, will the same amount of research will get done? If the university stopped getting indirect costs paid, I imagine all the staff listed above would probably be fired. Instead I would be expected to arrange for those things myself … My guess is that I would not be able to get as much research done.  Instead I would spend time, hiring my own lab accountant, meeting delivery people, checking the orders, and taking them where they need to go, getting quotes for building repairs, I might need a greenhouse technician for my lab, I would spend time paying my lab internet/phone/electricity/gas bills each month, and then I would have to make sure that the floor in my lab gets mopped at least once and a while.  Whether I do these things, or my graduate students help out here and there, less research would get done in the same period of time.  

Moreover, since all the things listed above are the true cost of my research, I would simply have to build them into my grant budget. Now the list of three things that cost $X, is a much longer list that surely costs more than $X. Could I organize these things individually cheaper than the university can collectively?  I suspect not.  So, either grants will have to cost the government even more than they are now, or less research will get done.  Frankly I feel like the indirect costs I pay are a bargain.

(Post script: I’m under no illusions that I am entitled to grant money. Society may decide that they don’t want to fund as much research as they have in the past, in which case grant budgets should shrink. That is a perfectly legitimate thing for society to decide. This post is responding to the claim that indirect costs could be cut, and maintain the same level of research. I believe this to be untrue.) 

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The Effects of Prey Variation on Ecological and Evolutionary Oscillations

Post by Abdel Halloway

A new research article in the journal The American Naturalist is shedding light on the interesting dynamics between predator and prey. One of the most well-known phenomenon in ecology is cyclical nature of predator and prey populations. This is because predator and prey populations have opposite reactions to their counter-population. Predator populations increase with larger prey populations but prey populations decrease with large predator populations. As prey grow in population, so do predators which cause the prey to decrease causing the predators to decrease which allows the prey to grow in population. These ecological cycles are quite well-known and examples are present in the natural world (see Canadian Lynx-Snowshoe Hare). What is more hidden is how evolution affects these cycles.

Mathematicians have often known that evolutionary cycles are often present in predator-prey games. What has been less known are the conditions that drive evolutionary cycles. Matching games -- where the predator is most efficient at capturing prey when they have the same strategy -- is one type of game where evolutionary cycles are quite frequent. But knowing general conditions that may affect all types of games has been lacking. This new paper (Cortez, 2016) seeks to address that. It looks at how genetic variation affects both the evolutionary and ecological cycles between predator and prey.

To look at the importance of genetic variation, Cortez constructed a an evolutionary game different from the matching game. In it, only the prey evolves with defense being the evolvable trait but there is a trade-off of mortality and fecundity. Put simply, prey can invest in defense against predators but investing in defense takes away from their reproduction. At low predator populations, it’s better to have less defense as that means higher reproduction but at high predator populations, more defense is better since it lowers mortality. As predator populations oscillate, not only does the prey population oscillate with it but so does the amount of defense. As predator populations increase, prey become more defensive and the opposite as predators decrease. How though does genetic variation of this defensive trait within the prey population drive eco-evo cycles?

To get at this question, Cortez mathematical analyzed the eco-evo system breaking them down into two subsections: just ecology and just evolution. Each subsection could be analyzed to see how primed they were for oscillations using the property of stability -- an unstable subsystem is primed for oscillations while a stable one is not -- with each subsystem’s stability being independent of the other’s. For example, the evolutionary subsystem might be stable while ecologically it is not; or vice versa; or both could also be on the same wavelength. This leads to four cases to explore the effects of genetic variation: unstable-unstable, stable-unstable, unstable-stable, and stable-stable (eco-evo respectively). 

So how does this break down for each case? Well firstly, case 1 is an uninteresting case. Oscillations occur regardless of the amount of variation when both subsystems are unstable. It’s the other three cases where variation matters. And specifically, evolution has no influence over ecology if there is low variation. If the ecological subsystem is not going to oscillate and genetic variation is low, the whole system will not oscillate even if the evolutionary subsystem is unstable. For evolution to have any effect on ecology, genetic variation must be high enough.

Case 2 (stable-unstable) offers more dynamism but is pretty straightforward. High genetic variation will lead to evolution overcoming ecology and oscillations will develop with the effects become permanent above a certain threshold of genetic variation. The interesting results come in cases 3 and 4. These depend upon the interaction between ecology and evolution, specifically how the evolution of the prey’s defense affects its overall fitness.

If the ecological system is unstable but the evolutionary system is stable (case 3), then high genetic variation can actually stabilize the system. This can happen because of stabilizing selection -- when having either more or less defense than the general population leads to lower fitness. When this occurs, evolution overpowers ecology to create stability. In case 4 where both subsystems are stable, we get an even crazier result. In this case, if we have disruptive selection (the opposite of stabilizing selection), then that effect can overpower both ecology and evolution to get oscillations. But there’s one more twist in the tale. If the stabilizing or disruptive selection is really strong, then after reaching a certain threshold, the effects mentioned become permanent, similar to case 2. If the selection is weak though, then there is a second threshold of genetic variation which flips it back to its original state. For example, in case 3, if the stabilizing selection is moderate, moderate genetic variation leads to no oscillations while both low and high genetic variation leads to oscillations. The reasons for this strange effect were never explained.

The effects of genetic variation (and evolution more generally) on predator-prey cycles have been mostly hidden to ecologists. Evolution is always slower than ecology and occurs on timescales that are usually imperceptible to humans. This paper offers a general framework and theory by which ecologist can now identify how and why their study systems respond in certain ways. Now the question becomes what happens when predators can evolve?

Cortez, M.H. 2016. How the Magnitude of Prey Genetic Variation Alters Predator-Prey Eco-Evolutionary Dynamics. The American Naturalist

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A new co-contributed blog!

Post by Gord McNickle

Well, I’ve had this blog for about 3 years. In that time, I’ve started a dozen or so posts that I haven’t got around to finishing, and I’ve managed post only 4 entries. Not a very exciting blog. So, a few of my colleagues and I have decided to transform this into a co-contributed blog ( I want to thank Abdel for pushing this plan forward). We’re not too sure how this will take shape right now, but hopefully there will be more than 1.3 posts per year, and there will be some more interesting things than you will find in my head alone.  Stay posted and let me welcome my new co-contributors, in alphabetical order.  

Ray Dybzinski – Loyola University Chicago.

Abdel Halloway – University of Illinois at Chicago

Gord Mcnickle – Purdue University

Paul Orlando – Purdue University.

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Is the rate of retractions really increasing? Unfortunately, yes. But they are still very rare.

Post by Gord McNickle

This morning I saw this article from the New York Times with the title: “Retracted Scientific Studies: A Growing List”.  It prompted me to tweet this: 

.@NYTScience Here's a faster growing list of all the papers that haven't been retracted: https://t.co/iKy0t2gk1M https://t.co/0qeGzZrNPy

— Gord McNickle (@EcoEvoGames)

May 29, 2015

The NYT article cites a Nature news article which reported a 10-fold increase in the number of retractions between 1977 and 2009. I found a similar article in PLOSone that also plotted absolute numbers of retractions.  I get annoyed when politicians say things like “this number is the biggest number of all time” without the proper context which is what prompted my tweet. The total number of retractions might be increasing, but the total number of papers published is also increasing. The key thing to ask then is not how the absolute number of retractions is increasing, but how the proportion of retractions are increasing with time. But, I got to thinking: Is the rate of total publications increasing faster than the rate of retractions? Maybe I was wrong in my tweet, but this is a testable hypothesis! So, instead of working on my important deadlines this morning, I tried to estimate the proportion of papers that have been retracted with time.

These numbers are quick and dirty; this is a blog written over coffee not a peer reviewed article. Here’s what I did: Using the Web of Science, I searched for “Retracted Article” and “the*” in titles.  This means that my estimates of total papers published in each year are going to be under-estimates. But unless you have reason to think that the occurrence of the term “the*” in titles is systemically biased over time (and if you have reason to think this I would love to hear why!), then this will at least give us estimate of the rate of increase in the total number of papers each year. This should provide a glimpse into whether the proportion of retractions are increasing or decreasing over time. In other words, this will not give an accurate estimate of the true rate, but it should give a reasonable estimate of whether this proportion is increasing or decreasing with time.  So use these numbers qualitatively, not quantitatively. Here’s what this turned up:

I was able to pretty closely reproduce the total number of retractions reported in that Nature News article and the PLOSone article. The occurrence of the word “the*” over time, which is our rough and dirty estimate of the total number of papers published over time, is also increasing with time and is reasonably close to what they report in the PLOSOne paper, but is indeed an underestimate.  But when we put the two together, we see that the proportion of papers being retracted each year is also increasing with time. So, perhaps science has a problem after all. There are lots of bad reasons, neutral reasons and perhaps even onen or two good reasons why this proportion might be increasing with time. However, I don’t really want to speculate about the causes of these patterns. 

Two more points: First, if my estimates of the total number of papers published are underestimates, then the proportion of papers retracted is even lower than in my figure above. So, even though retractions are going up they are still INCREDIBLY rare. Second, 17,959,139 articles have been published since I was born ... and that's an underestimate! Amazing.

The final point?  It is with great regret that I must retract my tweet from earlier this morning.  

(Here is the code to reproduce that figure, which includes my raw “data”. Note that I didn't plot the data past 2010 because I imagine it probably takes some time for a reason to retract a paper.)

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Matrix games remain an important tool in evolutionary game theory

Post by Gord McNickle

In my last post, I wrote about two things that I think limits the uptake of evolutionary game theory as a more general tool in ecology and evolution: (1) people I meet often think the 2x2 matrix games we teach in introductory courses are the only way to do game theory and; (2) people I meet also often seem to think game theory can only apply to behaviour. At the time I threatened to expand on these in future blog posts. Last week I went to a workshop on evolutionary game theory hosted by the Mathematical Biosciences Institute at Ohio State University. So, I figured that now would be a perfect time to revisit one of these. Specifically, I want to write about matrix games.   

In my last post I believe I called matrix games “1970s technology”. However, I was surprised by how many people at the workshop were generating really powerful and novel predictions using matrix games. The outstanding talks have me regretting my previous post!  So, even though I mentiond these in my previous post: let me highlight two applications of matrix games that really captured my imagination at the workshop and I think highlights their usefulness: First, when strategies are categorical, then matrix games are your tool. Second, when you’re trying to understand how something might evolve at all, that is to say, the presence or absence of something; matrix games are a great tool. 

Categorical strategies: Rock-paper-scissors and mating systems

Animals often have mating systems where one sex will have several alternative strategies. The classic example is to have: (1) some kind of dominant male that is big and/or aggressive and/or very showy and as a consequence attracts most of the female choice and; (2) some kind of sneaker or satellite male strategy that is smaller, and/or less aggressive and/or less showy that is able to coexist via frequency dependent selection by, well, sneaking some copulations. These two male strategies (and sometimes a third male or even a female strategy) are very clearly categorical and best described with a matrix game.  At the workshop, Barry Sinervo described how he and his colleagues have been applying the rock-paper-scissors game to understand the coexistence of these alternative mating strategies and has catalogued a huge number of really interesting examples. Barry has done an enormous amount of empirical and theoretical work that is really exciting. I won’t try to explain any further, if this seems interesting to you, here are some of Barry’s papers to get you started: www.dx.doi.org/10.1038/380240a0 www.dx.doi.org/10.1146/annurev.ecolsys.37.091305.110128

The presence or absence of a trait: The evolution of personality

There is a lot of attention being paid lately to the concept of personality. Personality is often defined as something like consistent/stable individual differences in behaviour within a population. It’s probably not surprising that there might be variation in strategies within a population (this is the essence of behaviour!), but what is surprising about personality is that this variation becomes fixed and consistent within an individual. If multiple strategies are evolutionarily stable (e.g. Hawk and Dove), one might expect that the most adaptive solution would be to simply exhibit plasticity within an individual where sometimes you are a hawk and sometimes you are a dove. Yet, personality means that apparently this often doesn’t happen; instead we get individuals that are either hawk or dove. Why? Sasha Dall explained this using a variant of the Hawk-Dove game where there are players that always play hawk, players that always play dove, and then a third strategy that is a sort of eavesdropping mimic. We’ve known that multiple strategies can coexist for a long time, but it hasn’t always been clear whether we should have monomorphic strategy populations (i.e. flexible behaviour) or polymorphic strategy populations (i.e. personality). These matrix games are shedding light on this question! Again, here are some references: www.dx.doi.org/10.1073/pnas.161058798 www.dx.doi.org/10.1111/j.1461-0248.2004.00618.x

Matrix games live on!

There are many more examples where matrix games remain useful.  How can mutualism/cooperation evolve (here)? How can ritualized displays replace actual fighting in animals (here)?  When should plants defend their tissues (here)? Is rhizosphere priming by plants a mutualism or something else (here)?  And this is by no means an exhaustive list! So, I was terribly wrong when I called matrix games 1970s technology. There are many cases where we are interested in understanding categorical strategies, or simply making predictions about the presence/absence of a trait. If this is what you are interested in understanding, then matrix games are the tool you need to address these sorts of questions.

However, I think I got one thing right in my last post: There are many situations in ecology where you have quantitative traits, and my experience suggests that many people are unaware that continuous games exist which can model these quantitative traits. Indeed, my work primarily relys on continous games.  However, This will have to be the subject of a future post. 

[ecoevogames]

What is evolutionary game theory?

Post by Gord McNickle

If it’s not clear from the title of my blog, I use game theory as a tool to generate hypotheses for the study of plant ecology. I’ve increasingly come to the conclusion that game theory is the language of ecology, and that any general theory of ecological systems is probably going to have to be game theoretic. But, it’s also become increasingly clear to me that there is a bit of confusion out there about what game theory is, or is not.  For example, I was recently in one quite confusing conversation with an ecologist, where it gradually became clear they thought that there was nothing else to evolutionary game theory than the prisoner’s dilemma. I realised after much miscommunication that whenever I said “game theory” they thought I was saying “the prisoner’s dilemma” and that there were no other topics in the field of evolutionary game theory! That’s an obviously extreme example. More commonly over the years I’ve found that there are two very common misconceptions: 1) people don’t know continuous games exist and; 2) people think evolutionary game theory can only apply to behaviour.

1) Continuous games vs matrix games: I find that most people have a point of contact with at one or two simple matrix games like the Hawk-Dove game, or the prisoner’s dilemma. These are conceptually simple games that illustrate the basic logic of game theory and can be easily solved by undergraduates in introductory courses. These matrix games are what I often jokingly call 1970s technology. They are wonderful for teaching and learning some basic concepts surrounding game theory, and they still play a role in modern game theory, but in general they are woefully inadequate for predicting real systems. The basic problem is that the strategies in a matrix game are categorical.  You either are a hawk or a dove, you either cooperate or defect, there’s nuance, there’s no ‘in-between’.  Strategies in biological systems are more likely continuous. For example, how tall should a plant grow to over-top it’s neighbours? How much should I cooperate with a mutualistic partner? These strategies aren't categorical, they can take on any positive number within biophysical constraints. Don’t get me wrong, matrix games have their place. I often use them when I start thinking about a new problem, and they are especially useful if one simply wants to understand the presence or absence of a certain strategy (E.g. when might rhizosphere priming by plants be a form of mutualism?). But continuous games are more often the tool of choice if one wants to try and generate more useful and nuanced testable hypotheses because they describe the sorts of continuous traits that we are most often interested in studying as ecologists.  

2) Behavioural, ecological and evolutionary dynamics Unfortunately, if game theory is taught to undergraduates, it is almost exclusively taught as animal behaviour. Indeed, the hawk-dove game and the prisoner’s dilemma are mostly about the evolution and coexistence of behavioural strategies. Unfortunately, my guess (and it is only a guess) is that the problem is the word “game” and people picture something like checkers or hockey when they hear the word "game". This analogy of a game extends easily and obviously to behaviours, but less easily or obviously to coexistence in ecological communities.  But, evolutionary game theory seeks to find those strategies that are evolutionarily stable in that they cannot be invaded by rare alternative strategies. The solution can be one single ‘pure’ strategy, or several ‘mixed’ strategies that coexist. If the ESS is mixed, part of the ESS solution includes the prevalence of each strategy within the system. Importantly, for the application of evolutionary game theory to ecological systems a mixed strategy ESS can be achieved in two ways.       One way is for all the organisms to be at least somewhat genetically similar such that they are capable of all the ESS strategies, but they adopt different strategies in different contexts. Here, the different strategies would be exhibited with frequencies that match what I called prevalence above. I think this is the sort of behavioural game that most people are generally thinking of when they think of evolutionary game theory. This way of achieving the ESS can explain the evolution and coexistence of multiple strategies in eco-evolutionary time, but it can also capture one-on-one interactions in a game that plays out via behaviour during the lifetime of an organism. We come back to the checkers and hockey analogy.     The second way the ESS might be achieved is if the organisms are sufficiently genetically different that they are only capable of exhibiting one of the mixed ESS strategies. Now the prevalence of each strategy is actually the abundance of each strategy in a community, and strategies come and go via ecological replacement or by evolutionary change. It isn’t a stretch to imagine that the unique strategies that are only exhibited by one type of player in the system might be species, and the ESS lets us predict their abundances within the community. In my experience this second option is less well appreciated, but hopefully it is becoming clear how the ability to predict not only strategies (for example the wood, leaf and root production of tree species) but also their abundances within a community is an enormously powerful tool that evolutionary game theory provides to ecologists. For the life of me I don't know why people don't use it more often!

Over the next few weeks I hope to write a couple more posts that drills down on these two points to flush out more of the details. Stay tuned!

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I’ve started a blog: Here’s my philosophy of science

Post by Gord McNickle

This is my first blog post. So, I thought I would start all the way at the beginning and write a bit about some of the philosophy of science that really guides my daily approach to problem solving, and science. None of this is new; I’m sure you can find blog posts by the barrel from scientists pontificating about philosophy written all over the internet with permanent marker. But I really do think these ideas are critical for any scientist to keep on the tip of their brain. It matters how we do science, what we think science is, and it matters how we might make our approach to science better by thinking about these types of problems. It also matters how we gather evidence. So, here’s my take.

Most scientists have probably encountered Karl Popper’s idea of falsification as one solution to the problem of how to demarcate science from non-science. It’s often taught to scientists this way, but I always got the impression that Popper was a bit of a reluctant philosopher of science. If you read Popper closely, he was more of an epistemologist than a philosopher of science. His idea of falsification was conceived as a work around for the problem of induction. However, it can be useful to teach Popper’s falsification to young scientists to get them thinking in a hypo-deductive way even if they haven’t yet encountered Hume’s problem of induction. (That is, we want scientists to use deductive reasoning instead of inductive reasoning.) Popper’s falsification is a wonderful work around that does a reasonably good job of solving Hume’s problem of induction. Scientists absolutely should be wary of the problem of induction, and by formulating bold assertions or hypotheses and attempting to disprove them, we gain certainty that is impossible if we were to try and prove a hypothesis. We teach this to young scientists, but is falsification really what we do as scientists?

Many scientists might also be familiar with Thomas Kuhn’s ideas about paradigm shifts and the structure of scientific revolutions. Kuhn observed that, while scientists do seem to use Popperian falsification - in that they use the hypo-deductive method - they often don’t wholesale reject the major paradigms that guide their field (Kuhn defines paradigms somewhat vaguely via examples, a classic one being Newtonian mechanics versus Einstein’s relativity as alternative paradigms). Instead, scientists appear to treat certain individual falsifications as anomalies and it takes a lot of falsifications to shake the faith of a scientist in their overarching research paradigm. For example, an evolutionary biologist is probably prepared to accept that not all traits are adaptive, but they are probably not prepared to discard the theory of evolution by natural selection very easily. If Kuhn is correct, this gives a cyclical view of science where (after some early stumbling around with “precience”) scientists are engaged in what Khun called “normal science”. In normal science, everyone is working within a similar research paradigm, and the paradigm is working well to explain most data and most scientists are content. Individual falsifications are usually explained away. These falsifications are perhaps the fault of the observer or a problem with the instrument and so we don’t usually ditch our major paradigms. However, as these falsifications build up, the field reaches what Kuhn called a “crisis” and the anomalies in the data can no longer be ignored. When this happens, a new paradigm may arise. The new paradigm should be capable of explaining the old data, as well as the problem data that led to the crisis. Kuhn called such a shift in paradigms a scientific revolution, and once the revolution is over, we lead back into a period of “normal” science built upon the new paradigm until the falsifications start to build up again. It’s important to realize that Kuhn also thought that alternative paradigms were mutually exclusive. That is, alternative paradigms often synthesize the same observations, but in a fundamentally different way, and often with fundamentally different explanations and conclusions rendering them incompatible. One must be utterly abandoned before a second may arise. Kuhn’s book, the structure of scientific revolutions, is one of my favourite books, and everyone should give it a read.

At first glance Kuhn’s account might sound really good. We still have elements of the hypo-deductive method, but Kuhn’s account might be closer to how scientists actually use falsification. However, are Kuhn’s catastrophic revolutionary cycles really what goes on in science either? The idea of a paradigm as an immovable force that is only shaken by a “crisis” is probably not accurate. Instead, we scientists often make small (and ideally progressive) revisions over time in our thinking. Some hypotheses are discarded, while others might be retained. This can lead to incremental changes and revisions in a theory without one of Kuhn’s catastrophic crisis leading to a revolution. Instead, these quiet revolutions might occur quite slowly. It probably also isn’t true that alternative paradigms are wholly mutually exclusive. Newtonian mechanics has largely been replaced by relativistic mechanics, but there are some subsets of data (e.g. low velocity objects) for which both theories give a pretty similar answer. But can we do better still with our understanding of how science does work or should work?

You might also have heard of Imre Lakatos, though I find fewer people have heard of Lakatos than have heard of Popper or Khun. Lakatos was a student of Karl Popper, and he developed the idea of “research programmes” as a way of combining Popper’s falsification with Kuhn’s ideas of paradigms and revolutionary science. Personally, I think Lakatos has a really useful conceptual framework for thinking about science, and I would say I’m Lakatosian in my approach to science. The real key difference between Kuhn and Lakatos is the idea of hard core and auxiliary hypotheses as an essential feature of a scientific research programme. Hard core hypotheses are those that cannot be discarded without abandoning the entire research programme – this can still lead to Kuhn’s scientific revolutions if the hard core hypotheses are abandoned and then replaced. Where Lakatos and Kuhn (and Popper) depart are with auxiliary hypotheses. Auxiliary hypotheses are those that scientists test and are prepared to discard based on experimental falsification – and this retains Popper’s falsification. This account of science seems to be much closer to how we actually behave as scientists and solves many of the objections raised above. But I think it also tells us something about how we should behave. Lakatos made a distinction between progressive and degenerate research programmes. The distinction was primarily about explanatory power. Scientists working within a Lakatosian framework are constantly revising their world views as they discard auxiliary hypotheses, and adopt new ones. There is a danger here in this sort of ad hoc revisions (Popper was very concerned about such revisions, and thought that we should never use them!). Progressive research programmes are those that gain increased explanatory power through the constant abandonment of old auxiliary hypotheses and the development of new auxiliary hypotheses. Degenerate research programmes on the other hand are forced to make revisions to auxiliary hypotheses, but these revisions are more of a necessity to deal with troublesome data and do not lead to improved explanatory power. Another way to think about it is that degenerate research programmes are constructing auxiliary hypotheses to prop up the crumbling hard core hypotheses rather than to really gain an understanding of the world through falsification. A persistently degenerate research programme is headed towards something like a Kuhnian crisis, and this means that eventually the hard core hypotheses – and the entire research programme with them – will need to be replaced via a sort of revolution.

There is one more philosopher worth talking about in this modern world of “big data”. Francis Bacon was a 16th century philosopher, and an early scientist who was probably instrumental in the development of the modern scientific method. Bacon predates Hume’s articulation of the problem of induction, and was an advocate of reasoning using induction. This approach is directly different from Popper’s falsification. Where Popper advocated rejecting theories as a means to gain certainty, Bacon advocated the use of accumulating data to support theories via induction. Bacon actually wrote that "hypotheses have no place in experimental science", and thought that buy simply building up enough observations and synthesizing them we could gain understanding. This should seem troubling to you. We can never know whether explanations built in this way are true because of the problem of induction. I think that modern technology has allowed us to collect data at an unprecedented rate. I’ve met a number of ecologists who are very excited about this tool or that tool and ran out and collected a bunch of data but now don’t know what to do with it. They come and give talks in the department are actually searching for hypotheses after they collected the data. I’m slightly fearful that we are experiencing a bit of a Baconian revolution, and if you liked anything I wrote before this paragraph you should be worried about this.

Ok if you’ve lasted this long, you’re probably wondering: What good is any of this to a real scientist? I think there are four lessons here. The first lesson is that the basic idea of falsification is valuable: the problem of induction is real, and deductive reasoning is a much more reliable way of gaining knowledge. We’ve learned since the 16th century that Bacon’s inductive approach of searching for synthesis in data without a priori hypotheses is not very reliable if we want truth. I think that it is even more important than ever to be mindful of the problem of induction in this era of big data. However, though falsification gives a practical solution the problem of induction, it probably wouldn’t be a good idea to toss the major theories of our field out the window because of one strange experimental result and indeed, scientists don’t do this. The second lesson from Kuhn is to remember that all of our scientific theories are essentially wrong and are destined for the dustbin. As G.E.P. Box famously said: “all models are wrong, but some are useful.” It’s worth keeping this in the back of your mind as you falsify your favourite hypothesis and then have to think about why it got falsified. The third lesson comes from Lakatos who reminds us that it is wise to be worried about the ad hoc revisions that we make as scientists when faced with falsification of an auxiliary hypothesis. I think we should be conscious of Lakatos’ concepts of progressive and degenerate research programmes as we make these ad hoc revisions to our auxiliary hypotheses on a daily basis. Fourth, the ability to articulate the hard core and auxiliary hypotheses of your own research programme is an extraordinarily useful exercise. I tend to filter much of the scientific world through these four lessons, and I think it makes me better at problem solving.

Personally, I think ecology and evolution is near what Kuhn might call a crisis, and what Lakatos might perceive as a number of competing research programmes. But this post is already quite long, and so this will have to be part of a future blog post.

Acknowledgement: My B.Sc. was a in philosophy and ecology and I’ve worried about these things for a long time. However, a good portion of these ideas evolved via conversations with Joel S. Brown at the University of Illinois at Chicago.