David Ortiz Elected to Baseball's Hall of Fame!

The Baseball Hall of Fame results were announced today, and David Ortiz is the only person who was elected by the writers this year. As usual, you can see all of the ballots that have been made public so far at this website. You can also see a discussion of winners and losers from this year's results.

Two of the era committees elected several Hall of Famers last month.

This year, Roger Clemens, Barry Bonds, Curt Schilling, and Sammy Sosa were all in their 10th and final years of eligibility. Clems and Bonds crept up to 65% of the vote, but that's still below the 75% that is needed for induction. Schilling, given his repeated crap, lost many votes. With Clemens, Bonds, Schilling, and Ortiz no longer on the ballot next year and few newcomers of note joining the ballot, holdover Scott Rolen (who went up to around 63% of the vote this year) will likely be elected in 2023 (yay!). Newcomer Carlos Beltrán is the only new person on the ballot next year with any chance. The weak ballot will help him, but we'll see how the Astros cheating scandal affects his vote total. Todd Helton and Billy Wagner finally surpassed 50% of the vote this year, and I expect Todd Helton to make another big jump next year. Both merit election, but it it may take some time for Wagner and I think that Helton is more likely to be elected in 2024 than in 2023. Andruw Jones surpassed 40%, so he's also trending upward. Support for Omar Vizquel tanked because of his shenanigans, and he doesn't belong in the Hall of Fame anyway. Jimmy Rollins and Alex Rodriguez were the only other newcomers to the ballot besides David Ortiz to get at least 5% of the vote.

We will see how the era committees deal with Bonds, Clemens, and Schilling. They'll get into the Hall eventually (as they should, even with their horseshit), but it may take a while.

Update (1/26/22): Also see the voting round-up from Jay Jaffe.

Update (1/27/22): Here is Jay Jaffe's candidate-by-candidate breakdown of the 2022 voting.

Update (1/31/22): Here is Jay Jaffe's five-year outlook of the Hall of Fame balloting in the writers' ballot.

"A Multilayer Network Model of the Coevolution of the Spread of a Disease and Competing Opinions"

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

Title: A Multilayer Network Model of the Coevolution of the Spread of a Disease and Competing Opinions

Authors: Kaiyan Peng, Zheng Lu, Vanessa Lin, Michael R. Lindstrom, Christian Parkinson, Chuntian Wang, Andrea L. Bertozzi, Mason A. Porter

Abstract: During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease.

The `PrickRank' Algorithm

One way to gather information is to purposely write an incorrect 'factual' statement on social media.

People love to correct others (often obnoxiously, but at least one acquires info).

Google has PageRank, and social-media platforms like Twitter have this `PrickRank algorithm'.

(This monicker is destined to become a classic, just like FIPO.)

The Donkey Kong Visual Illusion

This visual illusion ought to be called the "Donkey Kong Illusion"

Horizontally aligned rows appear to tilt alternately. pic.twitter.com/80pfmXPql6

— Akiyoshi Kitaoka (@AkiyoshiKitaoka) January 9, 2022

"A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs"

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

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

Authors: Abigail Hickok, Yacoub Kureh, Heather Z. Brooks, Michelle Feng, and Mason A. Porter

Abstract: People's opinions evolve with time as they interact with their friends, family, colleagues, and others. In the study of opinion dynamics on networks, one often encodes interactions between people in the form of dyadic relationships, but many social interactions in real life are polyadic (i.e., they involve three or more people). In this paper, we extend an asynchronous bounded-confidence model (BCM) on graphs, in which nodes are connected pairwise by edges, to an asynchronous BCM on hypergraphs, in which arbitrarily many nodes can be connected by a single hyperedge. We show that our hypergraph BCM converges to consensus for a wide range of initial conditions for the opinions of the nodes, including for nonuniform and asymmetric initial opinion distributions. We also show that, under suitable conditions, echo chambers can form on hypergraphs with community structure. We demonstrate that the opinions of nodes can sometimes jump from one opinion cluster to another in a single time step; this phenomenon (which we call ``opinion jumping") is not possible in standard dyadic BCMs. Additionally, we observe a phase transition in the convergence time of our BCM on a complete hypergraph when the variance $\sigma^2$ of the initial opinion distribution equals the confidence bound $c$. We prove that the convergence time grows at least exponentially fast with the number of nodes when $\sigma^2 > c$ and the initial opinions are normally distributed. Therefore, to determine the convergence properties of our hypergraph BCM when the variance and the number of hyperedges are both large, it is necessary to use analytical methods instead of relying only on Monte Carlo simulations.