Note: one participant wasn’t getting a partner, so they opened up a new tab, fiddled with the id and played themselves. So we exclude the game with player id YSB8RYRgF2tQjym2e, which is game AQXAv4FKrZxBrTQgE

Summary

Each game has:

53 games in each condition

## # A tibble: 2 × 2
##   chat_cond     n
##   <chr>     <int>
## 1 chat         53
## 2 no_chat      53

Are they human?

## # A tibble: 2 × 6
## # Groups:   game_cond, chat_cond [2]
##   game_cond chat_cond    no   yes  `NA`   pct
##   <chr>     <chr>     <int> <int> <int> <dbl>
## 1 oct2024   chat         15    90     1 0.857
## 2 oct2024   no_chat      19    86     1 0.819

mostly think partner is human, oh good.

Before chat

People do talk to each other a little in the pre-game chat time.

Points earned

Note that BoS has a lower points range than PD because of what range of random numbers is selected.

Looks like chat does better in spike, and a little bit on normal BoS?

During chat

Not a lot of talking.

Per trial does more talking help

  • chat_0 is had access to chat and didn’t use on that trial
  • chat_1 is used chat on that trial
  • nochat_0 did not have access to chat

Looks like mostly the actually using the chat is what’s helping.

Looks like using language is correlated with better outcome with BoS and slightly better outcome for PD. But if this language helping or “people who avail themselves of the option to use language are more competent”.

Is there a dose-response relationship, or is one word enough?

Especially where we have more data, looks like one word is enough. Indicative of coordination rather than negotiation, probably?

Per game does talking help?

One idea is that talking on some trials may set up coordination strategies that can then effectively be used on later trials without talking on those trials.

So we want to look at overall volume of talking (in words or in # of trial talked) as a predictor for performance, controlling for talk on that round?

Looks like talking on other trials might help in BoS if you didn’t talk on this specific trial? But might just be fitting to outliers? Will need models.

Option selected

BoS

In BoS: P1 prefers AA to BB, P2 prefers BB to AA. AB and BA are 0 for both.

Near chance if you can’t talk, or if you don’t talk, far above chance if you do coordinate.

spike BoS

easy PD

In easyPD: P1 prefers BA > AA > BB > AB and P2 prefers AB > AA > BB > BA. AA is welfare maximizing.

Can get a reasonable option no matter what.

hard PD

In hard PD: P1 prefers BA > AA > BB > AB and P2 prefers AB > AA > BB > BA. AB/BA is welfare maximizing.

So if you do use the chat, you tend to get the best option. (Using chat means that easy and hard PD look different)

Whereas if you don’t chat, they look more similar at least.

Language

How much language?

Filter only for games that talked at least a little.

Second graph filters for trials that talked.

how often do both people talk

Even in games that talk, there aren’t that many trials where both people talk?

Subgroup analysis

As an exploratory thing, what if we look at the people who talked a lot or a moderate amount

high = 40%+ of trials (10 games)

med = 10% - 39% of trials (10 games)

minimal = < 10% of trials (33 games)

no = couldn’t chat (53 games)

Option

BoS

Spike BoS

easy PD

In easyPD: P1 prefers BA > AA > BB > AB and P2 prefers AB > AA > BB > BA. AA is welfare maximizing.

hard PD

In hard PD: P1 prefers BA > AA > BB > AB and P2 prefers AB > AA > BB > BA. AB/BA is welfare maximizing.

So if you do use the chat, you tend to get the best option. (Using chat means that easy and hard PD look different)