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.

"A Density Description of a Bounded-Confidence Model of Opinion Dynamics on Hypergraphs"

Another of my papers has now been published in final form. Here are some details.

Title: A Density Description of a Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

Authors: Weiqi Chu and Mason A. Porter

Abstract: Social interactions often occur between three or more agents simultaneously. Examining opinion dynamics on hypergraphs allows one to study the effect of such polyadic interactions on the opinions of agents. In this paper, we consider a bounded-confidence model (BCM), in which opinions take continuous values and interacting agents compromise their opinions if they are close enough to each other. We study a density description of a Deffuant–Weisbuch BCM on hypergraphs. We derive a rate equation for the mean-field opinion density as the number of agents becomes infinite, and we prove that this rate equation yields a probability density that converges to noninteracting opinion clusters. Using numerical simulations, we examine bifurcations of the density-based BCM's steady-state opinion clusters and demonstrate that the agent-based BCM converges to the density description of the BCM as the number of agents becomes infinite.

"Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling" (Second Edition)

The unnecessary second edition of the book Temporal Network Theory is now out. It includes a second edition of a chapter that I coauthored. Here are a few details.

Title: Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling

Authors: Dane Taylor, Mason A. Porter, and Peter J. Mucha

Abstract: We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change with time. We study supracentrality generalizations of eigenvector-based centralities, a family of centrality measures for time-independent networks that includes PageRank, hub and authority scores, and eigenvector centrality. We start with a sequence of adjacency matrices, each of which represents a time layer of a network at a different point or interval of time. Coupling centrality matrices across time layers with weighted interlayer edges yields a supracentrality matrix C(ω), where ω controls the extent to which centrality trajectories change with time. We can flexibly tune the weight and topology of the interlayer coupling to cater to different scientific applications. The entries of the dominant eigenvector of C(ω) represent joint centralities, which simultaneously quantify the importances of every node in every time layer. Inspired by probability theory, we also compute marginal and conditional centralities. We illustrate how to adjust the coupling between time layers to tune the extent to which nodes’ centrality trajectories are influenced by the oldest and newest time layers. We support our findings by analysis in the limits of small and large ω.

2023 Most Valuable Player Awards

Major League Baseball has announced its 2023 Most Valuable Players. To nobody's surprise, Shohei Ohtani of the Los Angeles Angels was the unanimous MVP in the Americal League. Also to nobody's surprise, Ronald Acuña, Jr. of the Atlanta Braves won the MVP award handily in the National League. Acuña, Jr. also won the MVP unanimously (which I hadn't expected), and this marks the first time that both MVPs were unanimous. Ohtani is the first baseball player ever to twice be name a unanimous MVP.

The National League MVP voting was interesting. Mookie Betts of the Los Angeles Dodgers got all 30 second-place votes, and Freddie Freeman (Dodgers) and Matt Olson (Braves) split all of the third-place and fourth-place voters (with Freeman getting 17 of the former and 13 of the latter to obtain 4 more points than Olson). Rookie of the Year Corbin Carroll of the Arizona Diamondbacks finished fifth in the voting and garnered 20 of the 30 fifth-place votes.

2023 Cy Young Awards

As with Major League Baseball's awards earlier this week, the 2023 Cy Young Awards were awarded to the expected pitchers. Blake Snell of the San Diego Padres won handily in the National League, and Gerrit Cole of the New York Yankees won unanimously in the American League.

2023 Managers of the Year

The Managers of the Year have been announced. Skip Schumaker of the Miami Marlins won in the National League and Brandon Hyde of the Baltimore Orioles won in the American League.

2023 Rookies of the Year

Baseball's 2023 Rookies of the Year are Gunnar Henderson of the Baltimore Orioles and Corbin Carroll of the Arizona Diamondbacks.

Both selections were unanimous, and it was clear that both selections would either be unanimous or very nearly so (and it was clear that Carroll would win unanimously).

What Happens in San Juan Capistrano Stays in San Juan Capistrano (2023 Edition)

I was just in San Capistrano for a bit more than a day to hang out with friends.

2023 Silver Slugger Awards

The 2023 Silver Slugger awards were announced today. This includes inaugural team awards for the Braves in the National League and the Rangers in the American League.

2023 Gold Glove Awards

Baseball's 2023 Gold Glove Awards were announced today.

What Happens in Pittsburgh Stays in Pittsburgh

I am off to Pittsburgh for a few days!

RIP Gary Lorden (1941–2023)

Gary Lorden, a profesor emeritus of mathematics at Caltech, died last night. In addition to being a mathematics professor, Gary held many leadership positions at Caltech. He was the only statistician in Caltech's math department, and I TAed for him during my junior year in the inaugural edition of the so-called "new core", which included major changes in Math 1 and Math 2. I got my gig as a consultant for the movie "Meet Dave" through Gary. He was also a very kind person.

Update (10/30/23): Caltech has posted a short obituary.

Nicolas Bourbaki and The Traveling Wilburys

Nicolas Bourbaki was basically the mathematics version of The Traveling Wilburys.

What Happens in Providence Stays in Providence

I am heading off to Providence to participate in the first roughly 1.5 days of ICERM's workshop on Mathematical Challenges in Neuroscience Network Dynamics.

A New Secondary Appointment in UCLA's Department of Sociology

As a small bit of career news, I now have a secondary appointment (i.e., a "0% appointment) in UCLA's Department of Sociology, in addition to my primary appointment in the Department of Mathematics. I am looking out to hanging out and otherwise interacting with the sociologists! I guess that I now get to consider myself an honorary sociologist?

"Minimizing Congestion in Single-Source, Single-Sink Queuing Networks"

Another of my papers has appeared in final form. Here are some details about it.

Title: Minimizing Congestion in Single-Source, Single-Sink Queuing Networks

Authors: Fabian Ying, Alisdair O. G. Wallis, Mason A. Porter, Sam D. Howison, and Mariano Beguerisse-Díaz

Abstract: Motivated by the modeling of customer mobility and congestion in supermarkets, we study queueing networks with a single source and a single sink. We assume that walkers traverse a network according to an unbiased random walk, and we analyze how network topology affects the total mean queue size Q, which we use to measure congestion. We examine network topologies that minimize Q and provide proofs of optimality for some cases and numerical evidence of optimality for others. Finally, we present greedy algorithms that add edges to and delete edges from a network to reduce Q, and we apply these algorithms to a network that we construct using a supermarket store layout. We find that these greedy algorithms, which typically tend to add edges to the sink node, are able to significantly reduce Q. Our work helps improve understanding of how to design networks with low congestion and how to amend networks to reduce congestion.

"Recurrence Recovery in Heterogeneous Fermi–Pasta–Ulam–Tsingou Systems"

Another of my papers was published in final form today. Here are some details.

Title: Recurrence Recovery in Heterogeneous Fermi–Pasta–Ulam–Tsingou Systems

Authors: Zidu Li, Mason A. Porter, and Bhaskar Choubey

Abstract: The computational investigation of Fermi, Pasta, Ulam, and Tsingou (FPUT) of arrays of nonlinearly coupled oscillators has led to a wealth of studies in nonlinear dynamics. Most studies of oscillator arrays have considered homogeneous oscillators, even though there are inherent heterogeneities between individual oscillators in real-world arrays. Well-known FPUT phenomena, such as energy recurrence, can break down in such heterogeneous systems. In this paper, we present an approach—the use of structured heterogeneities—to recover recurrence in FPUT systems in the presence of oscillator heterogeneities. We examine oscillator variabilities in FPUT systems with cubic nonlinearities, and we demonstrate that centrosymmetry in oscillator arrays may be an important source of recurrence.

"Non-Markovian Models of Opinion Dynamics on Temporal Networks"

One of my papers was published in final form today. Here are some details.

Title: Non-Markovian Models of Opinion Dynamics on Temporal Networks

Authors: Weiqi Chu and Mason A. Porter

Abstract: Traditional models of opinion dynamics, in which the nodes of a network change their opinions based on their interactions with neighboring nodes, consider how opinions evolve either on time-independent networks or on temporal networks with edges that follow Poisson statistics. Most such models are Markovian. However, in many real-life networks, interactions between individuals (and hence the edges of a network) follow non-Poisson processes and thus yield dynamics with memory-dependent effects. In this paper, we model opinion dynamics in which the entities of a temporal network interact and change their opinions via random social interactions. When the edges have non-Poisson interevent statistics, the corresponding opinion models have non-Markovian dynamics. We derive a family of opinion models that are induced by arbitrary waiting-time distributions (WTDs), and we illustrate a variety of induced opinion models from common WTDs (including Dirac delta distributions, exponential distributions, and heavy-tailed distributions). We analyze the convergence to consensus of these models and prove that homogeneous memory-dependent models of opinion dynamics in our framework always converge to the same steady state regardless of the WTD. We also conduct a numerical investigation of the effects of waiting-time distributions on both transient dynamics and steady states. We observe that models that are induced by heavy-tailed WTDs converge more slowly to a steady state than models that are induced by WTDs with light tails (or with compact support) and that entities with longer waiting times exert more influence on the mean opinion at steady state.

What Happens in Berkeley Stays in Berkeley

In a few hours, I'll have my flight to Oakland and then head over to Berkeley to spend most of September in residence at the institution formerly known as MSRI as part of the semester on Algorithms, Fairness, and Equity!

During this period, I'll spend a couple of days at ICERM for a workshop on mathematical neuroscience. I'll return close to the end of September for the start of our new school year (and will spend my first full day back figuring out what I'll do for the next day's lecture in my graduate-level mathematical-modeling course).

What Happens in Seoul Stays in Seoul

I have a couple-day pitstop in Seoul before heading back to LA.

What Happens in Tokyo Stays in Tokyo

Today I am off to Tokyo, where I will be participating in the ICIAM 2023 conference.

I'll be giving a talk on Monday.

What Happens in Daejeon Stays in Daejeon

I am flying to Asia for a couple of conferences. My first stop is Daejeon for an ICIAM satellite conference on stochastic modeling and data analysis for biological systems.

Fernando Valenzuela's Number is Finally Getting Retired Tonight!

Tonight the Los Angeles Dodgers are finally retiring Fernando Valenzuela's number 34. This is long overdue. Fernando is a Los Angeles icon.

No Dodger has worn Fernando's number since he left the team, and now no other Dodger will ever wear it again.

Update: Here is ESPN.com's article about the jersey retirement ceremony.

What Happens in Sunnyvale Stays in Sunnyvale

I am off to Sunnyvale for the weekend to attend the wedding of one of my former Ph.D. students. I have many friends in the area, so I will also hang out with some of them, given that I'll already be in the area.

RIP Sinéad O'Connor (1966–2023)

Sinéad O'Connor has died.

She was only 56. But, to be honest, I am surprised that she lasted this long. She seemed to always be struggling.

You can read more about her in her Wikipedia entry.

What Happens in Richland Stays in Richland

I'll soon be visiting Pacific Northwest National Laboratory as part of a Directory Advisory Committee.

Serendipitous Convergence of the Dodgers and Tarzan Boy

On a few occasions this year, I had noticed the Dodger organist playing Tarzan Boy, and I was wondering why.

I had thought it was for something like certain leaping catches in the outfield, but it turns out that it is specifically for rookie James Outman. I figured that out last night because they played it when he got a hit in his first at bat. (I revised my opinion from seeing this when Outman was at the plate and running for a hit, with his locks flowing.) I thought it might have been because of his luxuriantly flowing hair.

I decided to google it to confirm whether I was right, and indeed Tarzan Boy is played specifically for good James Outman action, although it seems to actually be because of a nickname that is catching on. (I hadn't known about eh nickname.)

I am very amused by the fact that this is a convergence between the Dodgers and Tarzan Boy, given how many people from my Lloyd House days at Caltech would associate each of those two things individually with me.

What Happens in Dallas Stays in Dallas

Well, unfortunately, I won't be making my connection (annoying flight delay), and I will be staying an unintended night in Dallas before resuming my journey in the morning.

But at least I won't be liveblogging from the Dallas airport, as I did 16 years ago.

"Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels"

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

Title: Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels

Authors: Grace J. Li and Mason A. Porter

Abstract: Agent-based models of opinion dynamics allow one to examine the spread of opinions between entities and to study phenomena such as consensus, polarization, and fragmentation. By studying models of opinion dynamics on social networks, one can explore the effects of network structure on these phenomena. In social networks, some individuals share their ideas and opinions more frequently than others. These disparities can arise from heterogeneous sociabilities, heterogeneous activity levels, different prevalences to share opinions when engaging in a social-media platform, or something else. To examine the impact of such heterogeneities on opinion dynamics, we generalize the Deffuant-Weisbuch (DW) bounded-confidence model (BCM) of opinion dynamics by incorporating node weights. The node weights allow us to model agents with different probabilities of interacting. Using numerical simulations, we systematically investigate (using a variety of network structures and node-weight distributions) the effects of node weights, which we assign uniformly at random to the nodes. We demonstrate that introducing heterogeneous node weights results in longer convergence times and more opinion fragmentation than in a baseline DW model. The node weights in our BCM allow one to consider a variety of sociological scenarios in which agents have heterogeneous probabilities of interacting with other agents.

"Lonely Individuals Process the World in Idiosyncratic Ways"

One of my papers that came out a couple of months ago now also has its final volume and page numbers. Here are some details about the article.

Title: Lonely Individuals Process the World in Idiosyncratic Ways

Authors: Elisa C. Baek, Ryan Hyon, Karina López, Meng Du, Mason A. Porter, and Carolyn Parkinson

Abstract: Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by other people. What contributes to such feelings in lonely people? We used functional MRI of 66 first-year university students to unobtrusively measure the relative alignment of people’s mental processing of naturalistic stimuli and tested whether lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: Lonely individuals’ neural responses were dissimilar to those of their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when we controlled for demographic similarities, objective social isolation, and individuals’ friendships with each other. Our findings raise the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness.

What Happens in Beaver Hollow Stays in Beaver Hollow

I am off to Beaver Hollow for our 2023 Mathematics Research Community on Complex Social Systems.

"Detecting Political Biases of Named Entities and Hashtags on Twitter"

One of my papers came out in final form earlier today. Here are some details. (This is in collaboration with computer scientists, and stylistically it is rather different from much of my work. However, you'll still notice my hand in it. :P)

Title: Detecting Political Biases of Named Entities and Hashtags on Twitter

Authors: Zhiping Xiao, Jeffrey Zhu, Yining Wang, Pei Zhou, Wen Hong Lam, Mason A. Porter, and Yizhou Sun

Abstract: Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

What Happens at "Snowbird" Stays at "Snowbird"

Today I am off to the "Snowbird Meeting" (aka the SIAM applied-dynamical systems conference) for the latest instantiation of my favorite scientific conference series.

2023 Inductees to the Rock & Roll Hall of Fame

The Rock & Roll Hall of Fame has announced its 2023 inductees.

Of the acts on the ballot this year, the ones for which I cast a vote are Kate Bush (who made it) and Cyndi Lauper, New Order/Joy Division, and Warren Zevon (who didn't).

Song Parody: "They're Still ODEs to Me"

I felt inspired. :)

What Happens at SOCAMS Stays at SOCAMS

Today I am off to Irvine for the 2023 version of the SOCAMS conference to celebrate applied mathematics in Southern California.

What Happens in Vancouver Stays in Vancouver

I am off to Vancouver today for a little while. I'll be giving a talk tomorrow at University of British Columbia, and I'll also be staying with friends and hanging out with them for a while! (On this day 10 years ago, I flew away to visit the same people.)

What Happens in Potsdam Stays in Potsdam

I am heading to Potsdam, NY to partake in the 6th Northeast Regional Conference on Complex Systems.

"Theorems" (to the tune of ' "Heroes" ', by David Bowie)

"Theorems" (to the tune of ' "Heroes" ', by David Bowie)

I, I will be pure
And you, you will use rigor
And nothing will take it away
We can show them, just for one day
We can prove theorems, just for one day

And you, you can have bounds
And I, I'll take infinite time
We're joint authors, and that is a fact
We're coauthors, and that is that
Mathematics'll keep us together
It will not be just for one day
We can prove theorems for ever and ever
What do you say?

I, I wish I could prove
Like my teachers, my teachers can prove
Though sometimes it's hard to keep it together
I can show them, for ever and ever
Oh I can prove Theorems, just for one day

I, I will be pure
And you, you will use rigor
And nothing will take it away
We can prove Theorems, just for one day
We can do it, just for one day

I, I can remember (I remember)
Standing, by the board (by the board)
Arguments, far above our heads (over our heads)
And we tried, and we were never ignored (never ignored)
Our mentors, were always on our side
Oh we can show them, for ever and ever
Then we could prove Theorems, just for one day

We can prove Theorems
We can prove Theorems
We can prove Theorems
Just for one day
We can prove Theorems

We're students, and nothing will help us
Maybe it's hopeless, then you better not stay
But we could graduate, maybe one day

Oh-oh-oh-ohh, oh-oh-oh-ohh, maybe one day?

"An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks "

An article of mine just appeared in final form a couple of days ago. Here are some details.

Title: An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

Authors: Unchitta Kan, Michelle Feng, and Mason A. Porter

Abstract: Individuals who interact with each other in social networks often exchange ideas and influence each other’s opinions. A popular approach to study the spread of opinions on networks is by examining bounded-confidence models (BCMs), in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other nodes’ opinions when they lie within some confidence bound of their own opinion. In this article, we extend the Deffuant–Weisbuch (DW) model, which is a well-known BCM, by examining the spread of opinions that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinions when they interact with neighbouring nodes and (2) break connections with neighbours based on an opinion tolerance threshold and then form new connections following the principle of homophily. This opinion tolerance threshold determines whether or not the opinions of adjacent nodes are sufficiently different to be viewed as ‘discordant’. Using numerical simulations, we find that our adaptive DW model requires a larger confidence bound than a baseline DW model for the nodes of a network to achieve a consensus opinion. In one region of parameter space, we observe ‘pseudo-consensus’ steady states, in which there exist multiple subclusters of an opinion cluster with opinions that differ from each other by a small amount. In our simulations, we also examine the roles of early-time dynamics and nodes with initially moderate opinions for achieving consensus. Additionally, we explore the effects of coevolution on the convergence time of our BCM.

The Dodgers are Finally Retiring Fernando Valenzuela's Uniform Number!

It took way too long, but the Los Angeles Dodgers announced today that they are finally retiring Fernando Valenzuela's uniform number. Given what Fernandro means to this franchise and this city, the team should have retired his number a very long time ago.

"The Professional Road that I have Traveled (so far)"

I was asked to write about my career trajectory for DSWeb, so I wrote this short article, which officially has the generic title of "Professional Feature — Mason A. Porter".

I really like my concluding sentence: "I want my mentees to continue to do excellent mentorship and research, be warm and kind-hearted, and not take any crap from anyone."

Scott Rolen Elected to Baseball Hall of Fame!

Yesterday, Scott Rolen was elected to Baseball's Hall of Fame in his 6th year of eligibility. He and Fred McGriff (who was elected to the Hall of Fame by an era committee in December) will be officially inducted into the Hall this summer. I am very pleased that both Rolen and McGriff are now in the Hall of Fame!

Scott Rolen is eminently merits his election, and it's great that he got in after several years of rising vote counts. Todd Helton and Billy Wagner made huge gains this year and should be joining the Hall in 2024. Andruw Jones and Gary Sheffield also made huge gains, although Sheffield is in his last year of eligibility for election by the writers in 2024 and is likely to instead be elected later by an era committee. It now looks like Andruw Jones will likely be elected by the writers in the next few years (but probably not in 2024) after getting under 8% of the vote (!) in his first year of eligibility. Jeff Kent, who was in his 10th and final year of eligibility, surged to 46.5% of the vote and is likely to be elected later by an era committee. Carlos Beltrán debuted on the ballot with 46.5% of the vote (matching Kent). I expect that Beltrán will get up to the high 50s in 2024, have some chance (but unlikely) of election in 2025, and probably be elected in 2026.

A strong set of players is debuting on the Hall ballot for the 2024 cycle. This set of players is led by Adrián Beltré, who will surely be a first-ballot Hall of Famer. Joe Mauer also is debuting on the ballot, but I think it will take 2 or 3 years (most likely 2, in my view) for him to get in. Chase Utley is also debuting in 2024. He'll make it eventually, but his counting stats don't stand out, so it's going to take a few years for him to get in (but I think that he will eventually.)

As in each of the past several years, I was closely tracking the Hall of Fame tracker during the past couple of months as writers released their ballots to the public.

Here is Jay Jaffe's recap of the voting results.

Here are some "way too early" predictions (from Bradford Doolittle David Schoenfield) of Hall of Fame results for the next few cycles. For the most part, my views are far closer to Schoenfield's than the Doolittle's.

Update (1/26/23): Jay Jaffe has written his annual ballot round-up of the candidates on this year's ballot.

Update (1/30/23): Jay Jaffe has written his 5-year Hall prognostication.

What Happens at the Joint Mathematics Meetings Stays at the Joint Mathematics Meetings (2023 Edition)

I am off to the in-person Joint Mathematics Meetings in Boston. I am giving three talks this year.

"The Topology of Data"

Our introduction to a topological data analysis (TDA) for a general physics audience was published today in Physics Today. Here are some details.

Title: The Topology of Data

Authors: Mason A. Porter, Michelle Feng, and Eleni Katifori

Lede: Topological data analysis, which allows systematic investigations of the “shape” of data, has yielded fascinating insights into many physical systems.