I just ran my woosy LLM player (not a long prompt, not very involved logic) on my CPU/GPU in a 10-round IPD game against 25 opponents. That agent’s node had a degree of 6 throughout. On my system, taking no special steps to do anything fast, this took 3 mins 40 secs.

Assuming linearity (possibly conservative, but then again GPUs are hard to use in parallel if you don’t have additional hardware sitting around to exploit) and assuming our contest has 25 LLM players (1 per student) also with average degree of 6, here’s a rough estimate for how long it would take to run games of varying lengths:

1. 10 iterations — 5500 secs = 91.7 mins = 1.5 hours
2. 100 iterations — 55,000 secs = 917 mins = 15.3 hours
3. 1000 iterations — 550,000 secs = 9167 mins = 153 hours = 6.3 days (Stephen’s original plan)
4. 1,000,000 iterations — 550,000,000 secs = 9,166,666 mins = 152,778 hours = 6365 days = 17.4 years (Garrett’s and Marina’s preference)

Quiz #7, an unexpected opportunity for you to earn more points, has been posted and is due on April 25th.

Quiz #6 has been posted! It is due midnight April 20th, but do not smoke weed while taking it. It is open-book, open-notes, and open-calculator (you’ll need one), and timed at 63 minutes. Good luck!

Apparently I forgot to push the parallelized version of the BVM code from last week; it is now in the github repo, in the file surg_par.py.

Your ideas are solicited for how the following aspects of the IPD rulebook should be specified:

1. What kind of random graph should the agents be put on at the start of the game? And what should the nodes’ average degree be when play starts?
2. How many rounds should the game last at minimum?
3. Once the minimum number of rounds has been played, what should the probability p be of each successive round being the last one?
4. How many dollars should each of these actions cost:
   1. Deciding to seek FOAF information from a neighbor?
   2. Refusing to provide FOAF information to a neighbor when requested?
   3. Providing incorrect FOAF information to a neighbor when requested?
   4. Performing one rewire operation (drop a current neighbor, pick up a new neighbor?)
5. How many rewire operations should be permissible each round?
6. What should the token limit be for prompts to the LLM? (you can specify an answer in characters instead of tokens if you prefer)
7. What should happen if an LLM agent exceeds the token limit?
8. How many dollars should players whose real-world initials are “GM” be penalized at the start of each round?

You may give ideas about how any of the above should be handled by sending me an email with subject line “DATA 420 IPD rule preferences” no later than noon on Thursday, April 16th. The class will vote on all rules in class that day.

Only one rule preference email (which may itself contain any number of rule preferences) will be accepted per student. If there is another aspect of the rulebook that you’d like to weigh in on, please mention that in your email as well.

Wow, congratulations to Miranda for being singled out for this honor!

My recovery is going quite well so far, but all the same I think I need another slow day before I’m ready to re-enter the rat race which is UMW campus. So office hours are canceled for Monday, April 13th, but I plan on resuming class and office hours as scheduled on Tuesday, April 14th, at the usual times.

For anybody following this developing story, my surgery seems to have gone well and the doctor is pleased. He did think, however, that Tuesday might be too aggressive to plan on a return to the classroom. I’m going to see how I feel. I might come in that day if I feel good enough (and that my voice can sustain a pair of 2-hour classes), or I might do Zoom, or cancel altogether. Stay tuned on this website for the latest details!

Here’s the example we did in class of using batch_run:

from mesa.batchrunner import batch_run

    ... your amazing simulation model here ...

params = {
    "N": 150,
    "leave_prob": np.arange(0,1,.1),
    "edge_prob": np.arange(0,.5,.1),
    "prob_blue": 0.5,
    "fig":None,
    "ax":None,
    "do_plot":False,
    "seed":None,
}

if __name__ == "__main__":
    results = batch_run(
        model_cls=Society,
        parameters=params,
        rng=range(10),
        max_steps=None,
        data_collection_period=-1,
        number_processes=None,
    )
   print(pd.DataFrame(results).groupby(['leave_prob','edge_prob'])['Step'].mean())

This is the version that does a double-parameter-sweep on two i.v.’s (independent variables). One way to plot this intelligently, btw, is to use a heat map, which has the two i.v.’s on either axis and then uses the color of each cell to display the d.v. Here’s my preferred method (which uses the seaborn plotting library, which you must pip install):

heat = (
    results.groupby(["iv_1", "iv_2"])["dv"]
       .mean()
       .reset_index()
       .pivot(index="iv_1", columns="iv_2", values="dv")
)
sns.heatmap(heat)

Aetna came through at the eleventh hour today, and said they would cover my surgery. So I’ll be under the knife tomorrow morning bright and early, and will be canceling all my campus appointments (including class and office hours) for this week.

If all goes well, I’ll be back in the saddle on Monday, and will have office hours that day as usual. Stay tuned for details.

A few people have inquired as to whether they can do homeworks 5 and 6 on the cpsc server. The answer is yes, with two caveats:

• Do realize that like all remote servers, the cpsc server is “headless,” meaning you get a command line to it but no graphical display. Thus you won’t be able to run the animation and see it run. You can, however: (1) save plots (with the plt.savefig("filename.png") command) and download them to your machine to view, and also of course (2) run time-intensive parameter sweeps.
• The default version of Python on cpsc is really old (version 3.10) and incompatible with the latest Mesa release (which uses the discrete_space system we’ve been relying on). There is, however, an updated version, which you can run by typing “python3.12” instead of “python“.

Homeworks #5 & #6 have been framed as one large, combined assignment, and are worth +80XP together. Even though this monster consists of two related-but-separate parts (which you can attack in either order) you’ll be turning in just one submission, as the assignment explains.

Observation: if procrastinating on a big assignment is bad, procrastinating on a double-assignment is twice as bad!

Have fun with this!

I’ve posted the raw data for the class graph in two edge lists:

• class_graph.csv — the original “people whose names you remember” graph
• class_graph_less_dense.csv — the second “people you’ve had a conversation with” graph

Each one of them contains rows with two comma-separated entries: the rememberer, and the remembered.

networkx can load such a .csv file into a graph via:

g = nx.read_edgelist("name-of-file.csv", delimiter=",")

as illustrated in the class_graph.py file I also pushed.



Have fun!

Using the “have had a conversation with” criterion, our class graph looks a lot more manageable (click to enlarge):

And here’s the degree distro:

On Tuesday (tomorrow), office hours will be:

1. Online, instead of in person (see Canvas for the usual Zoom link), and
2. 10:30-11:30am instead of the usual 10-11am.

Clone this repo:

https://github.com/divilian/IPD-LLM

and run the program as follows (for instance):

$ python cli.py 150 \
    --agent-fracs TFT 0.3 Mean 0.3 Sucker 0.4 \
    --p-same 0.1 --p-diff 0.01 \
    --num-iter 100 --plot

You can run “python cli.py -h“, too, to see all the command line arguments and what they mean.

Bonus points to anyone who can vary the parameters such that (1) they maintain at least 5% of all three non-LLM agent types (Mean, Sucker, and TFT), and (2) the tit-for-tat agents, on average, come out the winners.

Quiz #5 has been posted to Canvas, and is due as the month of March expires. It’s timed at 60 minutes and otherwise has all the normal rules. Good luck!

You guys did a really good job of remembering each others’ names. Clearly this was the wrong bar to set for creating an edge in our class graph, because the thing is super dense (click to enlarge):

I’d like to get a more meaningful graph for analysis. So, since I’m giving away XP like candy, here’s another way to earn an additional +1XP: send me an email with subject line “DATA 420 class graph” that has the first name of every student in the class that you can remember having a conversation with. It might have been a one-on-one conversation, or a group conversation in which you and the other student were both present.

The conversation can be of any length. For instance, this counts as “a conversation”:

Them: Yo.
You: S’up.

(If you’ve never had a conversation with anyone else in the class, that’s fine; you can just say “nobody” in your email.)

I’m expecting that with this new, higher bar for edges, most students will have a degree of between 0 and 8, instead of this craziness:

If you’re interested in pursuing a masters degree at Virginia Tech, consider checking out this information session about the program.

I’ve now posted the reflection paper assignment I promised, and I hope many of you will consider completing it. It’s due in a month, so this should give you plenty of time to get your creative juices flowing and to digest many things that are in the book.

One word of warning: grading for this paper will be rigorous. I am not looking for two pages of crap, or for two pages of a stream of consciousness. If you turn in crap, expect to get 5 or 10 XP for your grade, depending on the level of crap. To get a score more towards the maximum, I’m looking for excellence. Take pride in your work, give yourself enough time to succeed, and bounce ideas off myself or others, and you should be well-positioned.

Homework #4 is posted, and is due next week. There will be no extensions to this one, so don’t expect one. Time to get started!

Click for a close-up!

What do people think Samuel is doing in the picture? Here are the results:

I’ve pushed the schelling.py program from today to the class github repo, as well as proof for Olivia that yes, Thomas Schelling’s name really does have a ‘c’ in it.

Quiz #4 contained multiple brain farts by yours truly, and so for a couple of the questions you may see your answer raised. In particular, my logical thinking failed me in numerous ways on the last problem, so I have given everyone the full 6 points for this question. I’ll explain in more detail in class today.

Be advised that at least one of the answers to quiz #4 is incorrect. I will fix that and make amends as soon as I can get to it. In the meantime, don’t freak out about it, and just wait patiently for a correction.

Since campus is closed tomorrow, homework #3 will be due Tuesday, March 17th at 5pm instead of tomorrow.

Due to the campus closure, office hours will be on Zoom tomorrow at the usual time. The Canvas announcements have the Zoom link, which is the same as it’s always been for this class.

Quiz #4 has been posted to Canvas. It is timed at 60 minutes, and is closed-all-the-usual-stuff. Although it is due on Wed, Mar 18th at midnight, it only covers class material up through this past week. So you can take it any time!

Good luck!

The code from today’s class has been pushed to the class github repo. (sirabm.py).

I’ve decided to postpone homework #3‘s due date from Friday to Monday, March 16th. That way I can expect excellent work!

I will be holding Office Hours virtually (over Zoom) today, at the usual time (12-2pm). See the Canvas page for the Zoom link (which is the same Zoom link as always).

I’ve posted my solutions to the reading check I meant to give on Thursday (but wasn’t ready in time) to the readings tab. Check out what you missed — all two pages!! :-O