What Happens in Warsaw Stays in Warsaw

I am heading to Warsaw to participate a couple of days in the WAW 2024 conference. This is my first trip to Poland in several years, and unfortunately it's going to be very brief.

"Inference of Interaction Kernels in Mean-Field Models of Opinion Dynamics"

A paper of mine just came out in final form. Here are some details about it.

Title: Inference of Interaction Kernels in Mean-Field Models of Opinion Dynamics

Authors: Weiqi Chu, Qin Li, and Mason A. Porter

Abstract: In models of opinion dynamics, many parameters — either in the form of constants or in the form of functions — play a critical role in describing, calibrating, and forecasting how opinions change with time. When examining a model of opinion dynamics, it is beneficial to infer its parameters using empirical data. In this paper, we study an example of such an inference problem. We consider a mean-field bounded-confidence model with an unknown interaction kernel between individuals. This interaction kernel encodes how individuals with different opinions interact and affect each other's opinions. Because it is often difficult to quantitatively measure opinions as empirical data from observations or experiments, we assume that the available data takes the form of partial observations of a cumulative distribution function of opinions. We prove that certain measurements guarantee a precise and unique inference of the interaction kernel and propose a numerical method to reconstruct an interaction kernel from a limited number of data points. Our numerical results suggest that the error of the inferred interaction kernel decays exponentially as we strategically enlarge the data set.

Shōgun (2024)

I just finished watching the 2024 Shōgun, which I enjoyed very much.

I read the book around December 1988 — followed over the next years of reading every single other Asia-saga novel that James Clavell wrote — during my elementary school's winter break. I was so captivated that that was basically all I did during that winter break. I was already a slow reader back then, and now I read much more slowly than I did back then. (I also don't have time to basically only read a book nonstop for a couple of weeks.) I was fascinated by the epic combined with the portrayal of how East and West saw each other through their interactions. This was the first book in my life that had ever captivated me that way, and I was really excited when I saw a poster for the new miniseries a few months ago.

The 2024 series did a great job of capturing that, and it was aspects of those interactions and contrasting views (and part of the scene of peeing in a garden to consummate an agreement, and I am pretty sure that I know which scene in the new tv series corresponds to that vignette) that really stood out to me. However, most of this runs together through all of Clavell's works, and I can't really separate Shōgun from the others. I had forgotten almost all of the plot, but from Wikipedia it seems that the new series adapted it very well.

I never watched the 1980 miniseries. There was a 1988 miniseries of Nobel House. I also never watched that one, but I did notice Shōgun and Tai-Pan (and knew that Nobel House was by the same author, and Tai-Pan also caught my eye because of the Apple II game of almost the same name that was inspired by the novel) on a bookshelf in my parents' house (nobody else in the household had read these epic books), so I picked up Shōgun, which became an important part of my own personal history, even though I forgot so much of it.

I suppose that a new Nobel House miniseries may be possible? That one, too, was a particularly awesome book. (I also enjoyed the others, although I gave up on Tai-Pan the first time and started over and read it only a couple of years after, because I could put up with the rougher writing of that earlier work with the thoughts of it as a prequel to Nobel House.)

What Happens in San Francisco Stays in San Francisco (again)

I am heading to San Francisco for a cousin's wedding.

RIP David Goodstein (1939–2024)

David Goodstein (an emeritus physics professor at Caltech) died yesterday. This is the end of an era.

I watched many of The Mechanical Universe videos in high school. The beginning and end of each video showed Goodstein lecturing to students in the big Caltech physics lecture hall. I had Goodstein for Physics 1a (mechanics) in fall of my frosh year in that same lecture hall, and I remember how surreal it felt. That was one of my big "Wow, I am now at Caltech." things. Also, I came out of lectures feeling that I understood the material — but then I tried the homework and saw that I didn't actually yet understand it.

(h/t Barry Simon)

What Happens in Hanover Stays in Hanover

I am heading to New Hampshire for the first time ever. I'll be in Hanover to give the mathematics colloquium at Dartmouth College.

What Happens in New York City Stays in New York City

I'm heading off to New York City for the first time in many years. I'll be giving a talk at The Rockefeller University.

"Complex Networks with Complex Weights"

The published version of one of my papers came out today. Its title is one of my favorites among all of the papers that I've ever written. Here are some details about the paper.

Title: Complex Networks with Complex Weights

Authors: Lucas Böttcher and Mason A. Porter

Abstract: In many studies, it is common to use binary (i.e., unweighted) edges to examine networks of entities that are either adjacent or not adjacent. Researchers have generalized such binary networks to incorporate edge weights, which allow one to encode node–node interactions with heterogeneous intensities or frequencies (e.g., in transportation networks, supply chains, and social networks). Most such studies have considered real-valued weights, despite the fact that networks with complex weights arise in fields as diverse as quantum information, quantum chemistry, electrodynamics, rheology, and machine learning. Many of the standard network-science approaches in the study of classical systems rely on the real-valued nature of edge weights, so it is necessary to generalize them if one seeks to use them to analyze networks with complex edge weights. In this paper, we examine how standard network-analysis methods fail to capture structural features of networks with complex edge weights. We then generalize several network measures to the complex domain and show that random-walk centralities provide a useful approach to examine node importances in networks with complex weights.

Adrián Beltré, Todd Helton, and Joe Mauer Elected to Baseball Hall of Fame!

Adrián Beltré, Todd Helton, and Joe Mauer have been elected to the Major Legaue Baseball Hall of Fame! I knew that Mauer would make the Hall of Fame eventually, but he far surpassed my prediction for how he was going to do this year. I am pleasantly surprised to see him make the Hall on the first ballot, as I thought that he would need to wait a year or two to be elected. Adrián Beltré obviously sailed into the Hall on the first ballot.

Billy Wagner, who was named on 73.8% of the ballots, missed election to the Hall by only 5 votes. He'll make it in 2025, which is his 10th and final year on the writers' ballot. Gary Sheffield was named on 63.9% of the ballots in his final year on the writers' ballot. His Hall case is now in the hands of the various small commitees, and I think (and hope) that he'll make it eventually. Andruw Jones had a small gain to 61.6% and Carlos Beltrán made a sizeable gain to 57.1%. Beltrán has an outside shot to be elected in 2025, but I think that 2026 is more likely. Andruw Jones could also ultimately make it through the writers' ballot, but I think that Beltrán will surpass Jones in the vote total in 2025. One way or another, they'll both eventually make the Hall of Fame. Chase Utley got 28.8% of the vote in his debut on the ballot. He did much better in the public ballots than in the private ones. I do think that Utley will eventually make it, but it's going to be a long road for the more sabermetrically-minded folks to convince others that Utley belongs in the Hall of Fame.

In December, a small committee elected former manager Jim Leyland to the Hall of Fame.

As usual, I have been following the ballot tracker very closely these past couple of months.

A discussion of a few ESPN.com writers of this year's biggest winners and biggest losers, as well as an outlook on the 2025 ballot.

Of the players who can debut on the writers' ballot in 2025, the only plausible Hall of Fame candidates are Ichiro Suzuki and C.C. Sabathia. Ichiro will sail into the Hall of Fame in his ballot debut (and hopefully he'll be elected unanimously, but I am not holding my breath). Sabathia will eventually make it, but I think that it's going to take a few years (say, 4 years).

Update (which I forgot to include in the original text of this post): My prediction for the 2025 balloting is that Ichiro Suzuki and Billy Wagner will be the two players elected. I think that Carlos Beltrán will get around 70% of the vote next year and that Andruw Jones will be in the mid 60s (perhaps around 66%). I think that Chase Utley will probably end up at about 35%. Utley's candidacy appears to be the latest battle in the considerations of old-school versus new-school voters.

Update: Jay Jaffe has written a rundown of the results of this year's writers' ballot.

Update (1/24/24): Here is Jay Jaffe's candidate-by-candidate dissection of this year's writers' ballot.

Update (1/29/24): Here is Jay Jaffe's five-year forecast of Hall of Fame balloting.

"Learning Low-Rank Latent Mesoscale Structures in Networks"

One of my papers came out in final form today. Here are some details.

Title: Learning Low-Rank Latent Mesoscale Structures in Networks

Authors: Hanbaek Lyu, Yacoub H. Kureh, Joshua Vendrow, and Mason A. Porter

Abstract: Researchers in many fields use networks to represent interactions between entities in complex systems. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. In this paper, we present an approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of latent motifs that effectively approximate most subgraphs at a fixed mesoscale. Such low-rank mesoscale structures allow one to reconstruct networks by approximating subgraphs of a network using combinations of latent motifs. Employing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent motifs. The ability to encode and reconstruct networks using a small set of latent motifs has many applications in network analysis, including network comparison, network denoising, and edge inference.

Want Happens in San Francisco Stays in San Francisco

I am off to San Francisco for the 2024 Joint Mathematics Meetings.

Dodgers Sign Pitcher Yoshinobu Yamamoto!

The Dodgers have signed star pitcher Yoshinobu Yamamoto, who is joining the Majors from Japan. Now we have our ace starting pitcher!

This follows on our recent trade for pitcher Tyler Glasnow and our signing of free agent Shohei Ohtani.

What the Dodgers do is Moneyball with money.

"Human-Network Regions as Effective Geographic Units for Disease Mitigation"

Another of my papers just came out in final form. Here are some details.

Title: "Human-Network Regions as Effective Geographic Units for Disease Mitigation"

Authors: Clio Andris, Caglar Koylu, and Mason A. Porter

Abstract: Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.

2023 Hank Aaron Awards

The 2023 Hank Aaron Awards for the best offensive player in each league have been awarded to Shohei Ohtani (formerly of the Angels and now of the Dodgers) and Ronald Acuña, Jr. (of the Braves).

"Low-Dimensional Behavior of a Kuramoto Model with Inertia and Hebbian Learning"

A paper of mine just came out in final form. Here are some details.

Title: Low-Dimensional Behavior of a Kuramoto Model with Inertia and Hebbian Learning

Authors: Tachin Ruangkriengsin and Mason A. Porter

Abstract: We study low-dimensional dynamics in a Kuramoto model with inertia and Hebbian learning. In this model, the coupling strength between oscillators depends on the phase differences between the oscillators and changes according to a Hebbian learning rule. We analyze the special case of two coupled oscillators, which yields a five-dimensional dynamical system that decouples into a two-dimensional longitudinal system and a three-dimensional transverse system. We readily write an exact solution of the longitudinal system, and we then focus our attention on the transverse system. We classify the stability of the transverse system’s equilibrium points using linear stability analysis. We show that the transverse system is dissipative and that all of its trajectories are eventually confined to a bounded region. We compute Lyapunov exponents to infer the transverse system’s possible limiting behaviors, and we demarcate the parameter regions of three qualitatively different behaviors. Using insights from our analysis of the low-dimensional dynamics, we examine the original high-dimensional system in a situation in which we draw the intrinsic frequencies of the oscillators from Gaussian distributions with different variances.

Shohei Ohtani is Joining the Dodgers!

This is straight from the horse's mouth and is the largest contract in North American sports history.

Jim Leyland Elected to Baseball's Hall of Fame

Former manager Jim Leyland has been elected to Major League Baseball's Hall of Fame in a vote of the Contemporary Baseball Era Non-Players Committee.

"Leyland received 15 of a possible 16 votes (93.8%), while Piniella received 11 (68.8%), White received 10 (62.5%) and Gaston, Johnson, Montague, Peters and West each received fewer than five votes." A candidate needed to receive 12 or more votes (i.e., from at least 75% of the committee) to be elected.

Jay Jaffe wrote a particularly compelling case in favor of Bill White.

Shohei Ohtani Voted Major League Baseball's 2023 Top Designated Hitter

Well, duh.

2023 Relievers of the Year

Félix Bautista of the Baltimore Orioles and Devin Williams of the Milwaukee Brewers are this year's Relievers of the Year in Baseball.

2023 Comeback Players of the Year

Reliever Liam Hendricks of the Chicago White Sox and outfielder (and also infielder, when he was with the Dodgers) Cody Bellinger of the Chicago Cubs have been named Baseball's 2023 Comeback Players of the Year.