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Losing Face

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When Al makes an offer to Betty that Betty observes and rejects, Al may “lose face”. This loss of face (LoF) may cost Al utility, either directly or through reputation effects. This can lead to fewer offers and inefficiency in the context of bilateral matching problems. Thomas Gall discusses his a simple model with asymmetric information, a continuous signal of an individual’s binary type, and a linear marriage production function.

Image courtesy of interviewee. May 30, 2024

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This video describes a presentation about a research paper focusing on "matching markets" or "marriage markets," which involve how people form pairs or match up. The authors, Macau and the speaker, express their interest in exploring how human behavior, particularly fears of rejection and loss of face, affect the efficiency of these markets. They argue that traditional economic models often overlook these emotional factors, which can lead to inefficiencies and reduced matching activity—particularly impacting labor markets where shyness can hinder job applications and offers.

The research employs game-theoretic modeling to analyze the behavior of individuals in matching scenarios. Players in the model receive signals about each other's value (or type) before deciding whether to propose a match, with the authors suggesting that players will have "threshold strategies" for making these decisions. However, when loss of face is factored in, players may become more conservative and picky, which ultimately reduces overall matching activity and leads to potential economic consequences like increased unemployment.

Additionally, the paper discusses the implications of asymmetric loss of face, where one side of the market may be more affected than the other, and proposes potential policy measures to mitigate these effects, such as conditionally anonymous environments for proposals to alleviate fears of rejection.

The research finds parallels with real-life platforms like Tinder, which use algorithms that address these issues effectively. The authors emphasize the need for empirical studies to validate their theoretical findings, exploring how personal shyness and loss of face influence matchmaking and access to partners in various contexts. Ultimately, the study aims to highlight the significance of behavioral aspects in economic theories of matching markets.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.000 --> 00:01.685
Working on the paper, losing face

00:01.955 --> 00:04.285
because, we are interested in

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what we call matching markets

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or marriage markets, which sounds an awful lot more

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interesting, to non economist than industrial economists

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because for economists,

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this just means we're looking at markets

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where people meet essentially.

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And, they basically form pairs of groups.

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One of the more relevant, applications of

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that is the labor market where firms and workers meet

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and the firms are represented by, for instance,

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hiring committees, the workers.

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And obviously it is important

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for economic outcomes in the aggregate for,

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the persons involved, how efficient these meetings

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and matchings and assignments occur.

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And there's an awful lot of

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of work being done on search matching.

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Nobel prizes have been awarded

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and, a number of other interviewees have been talking

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about this, but, Macau

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and I were a bit, surprised that,

01:02.275 --> 01:06.475
all of these studies basically have not

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entirely looked at how human behavior shaped,

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and that's where the marriage market name is,

01:13.615 --> 01:16.315
is actually helpful because when we look at marriage market

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and where people meet,

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and then sometimes marriage a certain hesitance

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to ask the other other person out

01:26.855 --> 01:31.115
to basically reveal that you're interested in her or him.

01:32.305 --> 01:36.485
And, that is not something that an economic agents in,

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in our modeling would usually do.

01:38.265 --> 01:40.645
We are looking at rational agents

01:40.645 --> 01:42.685
and they're not inhibited by emotions.

01:43.185 --> 01:45.285
And then we were basically thinking about this is something

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that every one of us experiences being shy about asking

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somebody because we might be rejected.

01:53.155 --> 01:55.655
Um, and nobody has ever looked at that.

01:55.715 --> 01:58.415
So we thought this, this, this might really impede

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the workings of markets once we kind

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of like put this behavioral issue in.

02:03.995 --> 02:07.095
And that was basically how we came to do our work.

02:07.435 --> 02:10.455
And micro theorists by training ha have no,

02:10.485 --> 02:12.255
basically branched out and everything else.

02:12.795 --> 02:15.735
Um, but this was essentially back to my roots

02:16.775 --> 02:19.355
and we were basically employing theoretical research

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that builds on obviously, other contributions

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that other people have made, lab field experiments,

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data and so on.

02:29.255 --> 02:32.595
But mostly this is a theory paper where we apply

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game theoretic modeling in order to derive conclusions

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and predictions how these market will turn out when we admit

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for behavioral issues like fear of rejection, loss of face,

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the benefit of having a, a mathematical model in order

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to attacking these type of questions is essentially

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that we can measure how good solutions are.

02:57.595 --> 02:58.935
Um, by employing game

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Theoretical models, we can use the whole tool kits in

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economics that allows us to evaluate different policies

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or different solutions by their welfare that they generate.

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So that is the, the, the good thing about being theoretical.

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Oh, I love to talk about game theory.

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I ask a professor to talk about game three. I never stop.

03:20.525 --> 03:24.505
Um, so what we did is essentially we used some,

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asymmetric information game between,

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players on two sides of a market.

03:30.685 --> 03:32.985
And for convenience we labeled that male and female

03:33.295 --> 03:34.905
because the whole idea

03:35.005 --> 03:37.665
and the intuition comes from a marriage market.

03:37.685 --> 03:40.425
And it's not wrong to interpret our model as one

03:40.605 --> 03:43.345
of a marriage market where females and males meet,

03:43.365 --> 03:45.345
and then they basically have a look at each other

03:45.965 --> 03:48.825
and decide whether or not to propose game.

03:48.825 --> 03:52.995
Theoretically, this is modeled by private information

03:53.015 --> 03:54.995
of the players about their own type.

03:56.355 --> 03:58.725
They do not have information about the

03:58.725 --> 03:59.805
type of the other player.

04:01.065 --> 04:02.245
The types are important

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because we derive utility wealth from the type of the other.

04:07.105 --> 04:11.085
So we would quite like to match with the player

04:11.385 --> 04:13.805
of the other market side who has a higher type,

04:14.065 --> 04:15.525
has more value to us.

04:16.065 --> 04:17.405
That's why types are important.

04:17.425 --> 04:18.885
But we can't observe the types,

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we don't wear 'em on our foreheads.

04:20.825 --> 04:23.805
Um, we might wear the right type of watch,

04:23.905 --> 04:25.245
for instance on the other hand.

04:25.665 --> 04:29.645
So when we meet in our model, each of the player

04:29.645 --> 04:33.605
that meet receives a signal about the other player.

04:33.785 --> 04:36.245
And the signal is basically all kinds of information

04:36.515 --> 04:38.325
that we can basic digest

04:39.145 --> 04:42.365
and use to basically generate an idea

04:42.375 --> 04:43.485
about the other's type.

04:44.335 --> 04:46.315
So this is about information essentially

04:46.575 --> 04:47.795
before we meet, we have

04:48.415 --> 04:50.795
no specific information about the other player.

04:51.305 --> 04:55.195
When we meet, we receive a signal about that player's value

04:55.255 --> 04:59.315
for us and whether we would like to marry them essentially.

04:59.735 --> 05:01.955
Um, and that's essentially the model.

05:01.975 --> 05:04.275
And then we derived essentially the optimal actions

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and the optimal strategies of the player

05:07.215 --> 05:08.635
to remain game theory speed.

05:08.635 --> 05:13.235
And that basically means characterizing what kind

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of signals would induce a player

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to accept the other party.

05:20.785 --> 05:23.885
And that basically is the, the, the model we look

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for in Nash equilibrium,

05:25.255 --> 05:29.765
where both players basically behave in a way that's optimal

05:29.885 --> 05:33.605
for them, given that the other player also behaves in a way

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that's optimal, optimal for them given the

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other players' actions and strategies.

05:39.455 --> 05:41.955
The interesting thing about this is essentially

05:41.955 --> 05:44.715
that in the outcome our players use

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what we call threshold strategies.

05:47.175 --> 05:50.155
So they get a signal about the other player

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and if that signal is good enough,

05:52.335 --> 05:53.955
you go all in and propose.

05:53.975 --> 05:56.555
And yes, I would quite like to kind of continue with you,

05:56.735 --> 05:58.285
But we don't have loss of phase.

05:58.785 --> 06:00.885
Um, but yields sufficient outcomes.

06:00.985 --> 06:02.365
We are processing information

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and based on the information

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that we have, we make a decision.

06:05.305 --> 06:06.405
And this is mutual.

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So both of the players will make mutually efficient decision

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and the right quote unquote matches will form

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With loss of phase.

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We have a bit of a problem

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because players are inhibited,

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they're getting more conservative,

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they're getting more picky, in particular the high types.

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They will now raise the threshold that they require

06:29.005 --> 06:31.125
to be convinced of the other players worthiness.

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So they're getting more picky.

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And that means overall matching activity, goes down.

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So there are fewer successful matches in the economy.

06:40.185 --> 06:44.445
So if you translate that into labor markets, if loss

06:44.445 --> 06:47.725
of face played a role in the labor market in particular

06:48.065 --> 06:52.085
for employees who are looking for another post, then

06:53.065 --> 06:56.205
you have a direct consequence for unemployment

06:56.275 --> 07:00.545
because there's too little matching activities going on.

07:00.645 --> 07:02.825
Too few applications are stands out.

07:03.245 --> 07:07.185
Um, and we also find some interesting side effects

07:07.215 --> 07:10.465
that in particular when you have asymmetric loss of face

07:11.095 --> 07:13.985
that is one of the market side is affected

07:14.655 --> 07:15.745
more by the loss of faith.

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It could be it could come from the procedure.

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One market side has to, has to propose

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and the other one has to say yes or no for instance.

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And that means essentially they are, they don't have to kind

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of expose themselves.

07:27.125 --> 07:31.055
Um, then basically anything can happen.

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It depends a lot on the particular market,

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on the particular, situation

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and which kind of agents come together.

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So it could be that the market side

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who has the loss of face is actually better off

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because they become more, more, more picky.

07:49.075 --> 07:52.095
And that loss is actually born by the market side

07:52.095 --> 07:53.295
who doesn't have loss of face.

07:53.475 --> 07:55.015
So not having loss of face

07:56.345 --> 07:58.525
is not necessarily good for you.

07:59.505 --> 08:01.765
It if enough other people have it.

08:02.185 --> 08:05.685
Um, and then finally I think that is probably taking,

08:05.985 --> 08:08.405
taking us into policy territory.

08:08.555 --> 08:10.645
What can we do about it if it really exists?

08:11.105 --> 08:14.525
Um, we also consider a framework which we call conditionally

08:14.635 --> 08:16.525
anonymous environment.

08:16.785 --> 08:19.405
And that is basically a, a simple trick.

08:19.745 --> 08:24.365
We only reveal that, you have

08:25.045 --> 08:29.485
accepted a potential match to that potential match

08:30.145 --> 08:33.205
if the potential match in return has accepted you.

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So if you accept your match, reject

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your potential match will never learn

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that you would have had them.

08:44.275 --> 08:46.695
And that means essentially that sort of loss of faith

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where you suffer from revealing your interest.

08:50.995 --> 08:54.735
You believe that this could be a match, where this

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Loss of faith, ca cannot occur

08:57.365 --> 08:59.925
because the other party never learns

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that you've basically exposed yourself.

09:02.405 --> 09:05.705
Um, and that environment, we get the efficient outcome.

09:05.855 --> 09:08.185
That means essentially in matching markets on marriage

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markets where we have loss of faith, simple algorithms

09:13.875 --> 09:17.785
that act as intermediaries, that basically process

09:18.535 --> 09:19.705
accepts and rejects

09:19.925 --> 09:24.105
and only flag if a mutually acceptable matter has been found

09:24.395 --> 09:25.465
would solve the problem.

09:26.125 --> 09:27.545
And to our surprise,

09:29.125 --> 09:30.905
the market had already solved the problem

09:31.455 --> 09:33.905
because we found that the Tinder platform

09:35.405 --> 09:38.145
was based basically on this particular algorithm.

09:38.965 --> 09:42.145
So when we did the research, that was quite a few years ago.

09:42.145 --> 09:44.785
In particular when we started the research, Tinder wasn't

09:44.805 --> 09:46.305
as well known as it is now.

09:46.805 --> 09:51.225
Um, so, we were quite pleased to learn that,

09:51.985 --> 09:54.945
actually our, our research had a counterpart in the real

09:54.945 --> 09:56.785
world and, and a pretty relevant one.

09:57.165 --> 10:00.305
But of course you can also think about, other type of,

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of matching markets.

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Think of speed dating between innovators.

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Uh, think about venture capitalists,

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or think about researchers who are trying to kind

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of find partners on projects.

10:13.405 --> 10:14.985
You don't want to go out expose yourself.

10:15.005 --> 10:16.945
Oh, I really want to work with you only

10:16.945 --> 10:18.265
to find, no thank you.

10:18.375 --> 10:20.025
That was very interesting, but no, thank you.

10:21.685 --> 10:24.065
Um, so that is kind of the type of policy

10:24.125 --> 10:26.345
or real world application that we thought might be.

10:27.285 --> 10:31.105
The one thing that we probably need to deliver on, is

10:31.565 --> 10:33.545
to produce some empirical backup

10:33.965 --> 10:35.585
for our theoretical research.

10:35.725 --> 10:39.695
So what we would quite like to do permitting funding

10:39.835 --> 10:41.535
and all other things, is

10:41.635 --> 10:45.375
to take this research into the field or into the lab,

10:45.675 --> 10:48.735
and I think probably the field might be the place test it

10:48.755 --> 10:52.335
out, where we then could essentially, expose

10:53.175 --> 10:57.415
experimental participants to various environments

10:57.475 --> 10:58.575
and to see to what,

10:58.575 --> 11:01.295
what are the quantitative effects of loss of base?

11:01.645 --> 11:03.815
Does it exist? Is it important enough

11:03.815 --> 11:06.015
to really inhibit matchmaking, inhibit

11:06.595 --> 11:08.135
new connections being formed?

11:08.595 --> 11:12.815
Um, what's the percentage of connections that are lost due

11:12.815 --> 11:14.495
to personal shyness?

11:15.115 --> 11:19.015
Are different people more or less affected by loss of faith?

11:19.015 --> 11:21.775
Because there's also an inequality aspect or angle here.

11:22.155 --> 11:26.735
Uh, if particular groups in the population are more prone

11:26.755 --> 11:30.335
to loss of phase than others, then they might lose out

11:30.515 --> 11:32.255
or given our a non,

11:32.665 --> 11:35.775
asymmetric environment results, they might even win.

11:36.075 --> 11:39.295
So we, we might have a, we, we might have a problem in terms

11:39.295 --> 11:43.175
of access to other,

11:43.955 --> 11:46.615
we might have a problem in terms of access to partners

11:46.795 --> 11:49.495
and equal access to partners due to loss of phase.

11:50.075 --> 11:53.855
Um, so this is basically what we would think is the,

11:53.855 --> 11:54.775
our first priority.

00:00 - Working on the paper, losing face because, we are interested in what we call matching markets or marriage markets, which sounds an awful lot more interesting, to non economist than industrial economists because for economists,

00:12 - this just means we're looking at markets where people meet essentially. And, they basically form pairs of groups. One of the more relevant, applications of that is the labor market where firms and workers meet and the firms are represented by, for instance, hiring committees, the workers. And obviously it is important for economic outcomes in the aggregate for, the persons involved, how efficient these meetings

00:41 - and matchings and assignments occur. And there's an awful lot of of work being done on search matching. Nobel prizes have been awarded and, a number of other interviewees have been talking about this, but, Macau and I were a bit, surprised that, all of these studies basically have not entirely looked at how human behavior shaped, and that's where the marriage market name is,

01:13 - is actually helpful because when we look at marriage market and where people meet, and then sometimes marriage a certain hesitance to ask the other other person out to basically reveal that you're interested in her or him. And, that is not something that an economic agents in, in our modeling would usually do. We are looking at rational agents and they're not inhibited by emotions. And then we were basically thinking about this is something

01:45 - that every one of us experiences being shy about asking somebody because we might be rejected. and nobody has ever looked at that. So we thought this, this, this might really impede the workings of markets once we kind of like put this behavioral issue in. And that was basically how we came to do our work. And micro theorists by training ha have no, basically branched out and everything else. but this was essentially back to my roots

02:16 - and we were basically employing theoretical research that builds on obviously, other contributions that other people have made, lab field experiments, data and so on. But mostly this is a theory paper where we apply game theoretic modeling in order to derive conclusions and predictions how these market will turn out when we admit for behavioral issues like fear of rejection, loss of face, the benefit of having a, a mathematical model in order to attacking these type of questions is essentially

02:53 - that we can measure how good solutions are. by employing game Theoretical models, we can use the whole tool kits in economics that allows us to evaluate different policies or different solutions by their welfare that they generate. So that is the, the, the good thing about being theoretical. I love to talk about game theory. I ask a professor to talk about game three. I never stop. so what we did is essentially we used some, asymmetric information game between,

03:28 - players on two sides of a market. And for convenience we labeled that male and female because the whole idea and the intuition comes from a marriage market. And it's not wrong to interpret our model as one of a marriage market where females and males meet, and then they basically have a look at each other and decide whether or not to propose game. Theoretically, this is modeled by private information of the players about their own type.

03:56 - They do not have information about the type of the other player. The types are important because we derive utility wealth from the type of the other. So we would quite like to match with the player of the other market side who has a higher type, has more value to us. That's why types are important. But we can't observe the types, we don't wear 'em on our foreheads.

04:20 - we might wear the right type of watch, for instance on the other hand. So when we meet in our model, each of the player that meet receives a signal about the other player. And the signal is basically all kinds of information that we can basic digest and use to basically generate an idea about the other's type. So this is about information essentially before we meet, we have

04:48 - no specific information about the other player. When we meet, we receive a signal about that player's value for us and whether we would like to marry them essentially. and that's essentially the model. And then we derived essentially the optimal actions and the optimal strategies of the player to remain game theory speed. And that basically means characterizing what kind of signals would induce a player to accept the other party.

05:20 - And that basically is the, the, the model we look for in Nash equilibrium, where both players basically behave in a way that's optimal for them, given that the other player also behaves in a way that's optimal, optimal for them given the other players' actions and strategies. The interesting thing about this is essentially that in the outcome our players use what we call threshold strategies. So they get a signal about the other player

05:50 - and if that signal is good enough, you go all in and propose. And yes, I would quite like to kind of continue with you, But we don't have loss of phase. but yields sufficient outcomes. We are processing information and based on the information that we have, we make a decision. And this is mutual. So both of the players will make mutually efficient decision

06:10 - and the right quote unquote matches will form With loss of phase. We have a bit of a problem because players are inhibited, they're getting more conservative, they're getting more picky, in particular the high types. They will now raise the threshold that they require to be convinced of the other players worthiness. So they're getting more picky. And that means overall matching activity, goes down.

06:36 - So there are fewer successful matches in the economy. So if you translate that into labor markets, if loss of face played a role in the labor market in particular for employees who are looking for another post, then you have a direct consequence for unemployment because there's too little matching activities going on. Too few applications are stands out. and we also find some interesting side effects that in particular when you have asymmetric loss of face that is one of the market side is affected

07:14 - more by the loss of faith. It could be it could come from the procedure. One market side has to, has to propose and the other one has to say yes or no for instance. And that means essentially they are, they don't have to kind of expose themselves. then basically anything can happen. It depends a lot on the particular market, on the particular, situation and which kind of agents come together.

07:39 - So it could be that the market side who has the loss of face is actually better off because they become more, more, more picky. And that loss is actually born by the market side who doesn't have loss of face. So not having loss of face is not necessarily good for you. It if enough other people have it. and then finally I think that is probably taking, taking us into policy territory.

08:08 - What can we do about it if it really exists? we also consider a framework which we call conditionally anonymous environment. And that is basically a, a simple trick. We only reveal that, you have accepted a potential match to that potential match if the potential match in return has accepted you. So if you accept your match, reject your potential match will never learn that you would have had them.

08:44 - And that means essentially that sort of loss of faith where you suffer from revealing your interest. You believe that this could be a match, where this Loss of faith, ca cannot occur because the other party never learns that you've basically exposed yourself. and that environment, we get the efficient outcome. That means essentially in matching markets on marriage markets where we have loss of faith, simple algorithms that act as intermediaries, that basically process

09:18 - accepts and rejects and only flag if a mutually acceptable matter has been found would solve the problem. And to our surprise, the market had already solved the problem because we found that the Tinder platform was based basically on this particular algorithm. So when we did the research, that was quite a few years ago. In particular when we started the research, Tinder wasn't as well known as it is now.

09:46 - so, we were quite pleased to learn that, actually our, our research had a counterpart in the real world and, and a pretty relevant one. But of course you can also think about, other type of, of matching markets. Think of speed dating between innovators. think about venture capitalists, or think about researchers who are trying to kind of find partners on projects. You don't want to go out expose yourself.

10:15 - I really want to work with you only to find, no thank you. That was very interesting, but no, thank you. so that is kind of the type of policy or real world application that we thought might be. The one thing that we probably need to deliver on, is to produce some empirical backup for our theoretical research. So what we would quite like to do permitting funding and all other things, is

10:41 - to take this research into the field or into the lab, and I think probably the field might be the place test it out, where we then could essentially, expose experimental participants to various environments and to see to what, what are the quantitative effects of loss of base? Does it exist? Is it important enough to really inhibit matchmaking, inhibit new connections being formed? what's the percentage of connections that are lost due

11:12 - to personal shyness? Are different people more or less affected by loss of faith? Because there's also an inequality aspect or angle here. if particular groups in the population are more prone to loss of phase than others, then they might lose out or given our a non, asymmetric environment results, they might even win. So we, we might have a, we, we might have a problem in terms of access to other, we might have a problem in terms of access to partners

11:46 - and equal access to partners due to loss of phase. so this is basically what we would think is the, our first priority.

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