Sunday, July 24, 2016

Wednesday, May 11, 2016

Our new paper: Trenchard H., Perc, M. "Equivalences in biological and economical systems: Peloton dynamics and the rebound effect."

Our paper has just been published in PLOS ONE. This paper foreshadows what I anticipate will ultimately be a more important paper about energy savings mechanisms in biological systems, also co-authored with Matjaz Perc, currently under consideration for publication. When that paper comes out, I'll have more to say about some implications of what we discuss there in relation to what we discuss in this PLOS ONE paper. 

On a related side note, I am also hoping to hear definitive word soon on another paper prepared in collaboration with Andrew Renfree and Derek Peters from the University of Worcester, in which we apply our peloton model to groups of runners and test the effects of drafting on certain collective running dynamics. I have also now resumed work on a collaborative analysis of fish schooling dynamics that applies the peloton model.

So, in a sense this new PLOS ONE paper is a companion piece to the larger review paper in which we review literature on energy savings mechanisms in natural systems, and wherein we identify certain principles of the collective dynamics of pelotons that are common to other natural systems.

It is difficult to anticipate how our PLOS ONE paper may be viewed, if it gets much attention at all. I don't deny that we are somewhat perilously pushing the envelope of the peloton analogy into the realm of economics. Broadly speaking, some might argue that our attempts at identifying commonalities between certain economic parameters and biological ones are naive and audacious. There are risks to this interdisciplinary endeavor, but scientific breakthroughs are impossible without such risks and occasional failures. Regardless, clearly PLOS ONE and the reviewers have judged the analysis of sufficient merit to publish it for further appraisal in the wider cauldron of academic consideration.

A brief summary:
Our model of the rebound effect is premised on four main factors: 1. the price of the energy service, externally imposed upon the consumer; 2. the consumer’s maximum capacity (or budget) to pay for the energy service; 3. the reduction in cost to the consumer due to some energy service efficiency, as a percentage; 4. the rebound quantity, as a percentage. In our paper we identify the first three as the primary ones, but the fourth one is obviously of critical importance and discussed in detail in our paper.

These factors all have equivalences in peloton dynamics, respectively: 1. The speed set by a pacesetter in a non-drafting position (akin to the imposed cost of the service to the consumer); 2. The maximum sustainable output of a following cyclist in a drafting position (akin to the consumer’s available budget); 3. The energy savings quantity due to drafting, as a percentage (akin to the efficiency in the energy service that reduces the service cost to the consumer by some percentage); 4. Potentially some surplus energy facilitated by the energy savings mechanism of drafting that permits the cyclist to achieve higher speeds by drafting than she could without the drafting benefit (akin to the fraction of the consumer’s budget that has been freed for the consumer to purchase more of the energy service as a result of the reduction in cost of the energy service, than previously used).

By adapting a basic equation that describes the relationship of these factors in peloton dynamics, we have derived an analogous equation that describes the relationship between these four economic factors. Thus a ratio that models these factors allows us to identify two main thresholds that we have also identified in peloton dynamics.  The first one is clear: a decoupling threshold between the price of an energy service and a consumers ability to pay for it; the second is less clear but is based on upon the application of an analogous threshold in pelotons: the "protocooperative" threshold between one phase of collective behavior in which cyclists can share costly front positions, and a second phase of collective behavior in which cyclists can sustain the speed of a pacesetter only by exploiting the energy savings mechanism (drafting) but cannot pass the pacesetter in order to share the costly front position.

Our paper has just been published
Our paper has just been publishedI anticipate some criticisms of our approach and more particularly to our interpretation of the model. However, I feel it is prudent at this point first to hear what the criticisms may be before I try to anticipate and enumerate them here. My sense at this early stage is that the rebound ratio equation itself is defensible and, if there are problems in the paper, it isn't in the model itself, but in how best to interpret it. Our present interpretation of it and the effects of it may need to be modified in light of feedback and further analysis. 

If in the passage of time some or other aspects of our analysis turn out to be flawed, and if nothing else is achieved by our new paper in PLOS ONE, I hope that this singular point will stand the test of time: certain principles of peloton dynamics have application across a wide variety of systems, including not only a wide range of natural biological systems, but also among human-centric economic ones.  

Saturday, March 12, 2016

Come scientists and academics, publish while you still can; the times they are changing (with apologies to Bob Dylan).

It has been my intention to keep this blog focussed solely on peloton dynamics and its analogs, but I thought I would stray briefly across the boundaries and throw in my two cents worth about the historic third match yesterday between AlphaGo and master Go player Lee Sedol, summarized in this Wired article.

While I had not originally planned to watch the live game on the internet, serendipity led me to witness an unforgettable 3 1/2 hour game and move-by-move analysis by Michael Redmond, an American professional Go player, who has achieved the highest rank of this Asian dominated game. Certainly without Redmond's capable and animated analysis, the game would have been lost on me, although I could follow along roughly with my own crude evaluation of how the balance of advantage was unfolding. Redmond was accompanied by Go E-Journal Managing Editor, Chris Garlock, whose bias in favor of Sedol was palpable and contagious.

With Korean Lee Sedol down two games to nil in the best of 5 match, the third game was do or die for Sedol, and an historical milestone for the power of artificial intelligence, and for the AI and Go communities.

I am not a Go player, but growing up I learned the rules and played a couple of games with my brothers, who played among themselves enough to become competent players. I certainly remember the Ko rule, a situation in which players can alternate taking a single surrounded stone, but who must play an intermediate move elsewhere before mirroring the Ko move. Michael Redmond remarked that an AI Go player version from a few years ago ran into difficulty around Ko moves, which the underlying computer algorithm, based on Monte Carlo simulation methods, was not well equipped to handle. Redmond remarked, however, that even in October of 2015, AlphaGo demonstrated competence around Ko moves. Despite this, Redmond noted there were rumors that colleagues had advised Lee Sedol to induce AlphaGo into error by drawing AlphaGo into Ko moves that might be difficult for the computer algorithms.

Each player was allotted a total of 2 hours for their moves, with three 1-minute overtime periods per player, which re-started if a move was made before the expiration of the 1-minute period. With about 40 minutes remaining on AlphaGo's clock, Sedol was down to his three overtime periods, and Redmond was predicting an AlphaGo victory of about 60 to 30 (total territory claimed).

With one 1-minute period remaining (again, re-started if a move was made before expiry), Sedol displayed remarkable brilliance under enormous pressure by deftly guiding AlphaGo into a sequence of Ko moves. Many times Sedol placed his stone with one or two seconds remaining. Meanwhile, in the commentator's box, Redmond's hands were flying across the working board, demonstrating variations in play, computing the relative "liberties" (viable options surrounding a critical point of play), and continually declaring that AlphaGo had the advantage. The game was becoming increasingly complex, and the likelihood of Sedol making a crucial mistake was frighteningly high, while AlphaGo still had several minutes of regular play time in hand.

What Sedol did was utterly brilliant, but what I witnessed was almost a kind of AlphaGo mockery. Twice Sedol and AlphaGo exchanged Kos, until AlphaGo surprisingly placed a stone in an uncontested opposite corner of the board. Redmond and Garland, scrutinizing variations, missed seeing AlphaGo's move, and when they looked back at the actual game board to the locus of Ko exchanges, they were momentarily confused as to where AlphaGo had placed its stone. Earlier, Redmond pointed out that once AlphaGo computes the probabilities of an advantage in a particular region of play, if it "feels" it is ahead, it will place a move elsewhere on the board. At an earlier stage of play, AlphaGo made another such move, which Redmond said actually allowed Sedol to recover from a losing position.

On one hand we see how amazingly brilliant is the mind of Sedol, a man with a human brain, knowing that he can apply human pattern recognition and intuition, against a massively powerful computer, programmed with learning algorithms that can play itself continuously and update optimal solutions over the course of millions of iterations. Yet, when AlphaGo made its seemingly casual move in the uncontested corner in the closing moves of the game, preceding Sedol's resignation a few moves later, for me it was a crushing recognition that artificial intelligence has advanced into a realm in which there are no longer problems or fields of human inquiry that cannot be solved by artificial intelligence.

For instance, Google has access to vast libraries of scientific and academic journals, in addition to massive quantities of data that are constantly, and at increasing rates, being uploaded into the Cloud. With such data and access to knowledge, algorithms need only be implemented to ask questions about what information or solutions are missing from the scientific literature, and then in turn to synthesize the vast store of available information in conjunction with enormous quantities of data, and to answer itself the questions that it poses.

This makes me anxious. I don't know how scientists generally feel, but in the context of my own miniscule contribution to human knowledge, whatever that may be, I sense a sudden and sky-high spike in urgency now for humanity to maximize its creative resources in the sciences and all of academia. Study, learn, be bold and creative and push the boundaries of knowledge now; discover, cogitate and publish while you can before your quests and thirst for knowledge are quenched far more rapidly and adroitly by machines with names like DeepBlue and DeepMind. Take the chance now, or it won't come again.

While there will always be ways for humans to satisfy their intellectual hunger, to justify their lives, to seek their own unique place amid the universal struggle to balance suffering and happiness, to me the defeat of Lee Sedol by AlphaGo represents a cross-roads for science and the human quest for discovery, as the form of the human contribution to science is bound to look very different in the coming years.