Speaker 1: So what if I told you there's an inescapable mathematical
Speaker 1: proof explaining why the person who makes the first move
Speaker 1: in a relationship or you know, like the opening offer
Speaker 1: and a negotiation almost always comes out ahead.
Speaker 2: And to be clear, it is not about psychology at.
Speaker 1: All, Right, it's actually hardwired into the math of how
Speaker 1: systems organize themselves. So welcome to this deep dive, custom
Speaker 1: tailored just for you. Today. We're opening up a really
Speaker 1: fascinating source, the authoritative text book called Algorithm Design by
Speaker 1: John Kleinberg and Epit Tardos. And I mean our mission
Speaker 1: today is not about writing lines of code or anything
Speaker 1: like that.
Speaker 2: No, not at all. It's really about looking at algorithms
Speaker 2: as a powerful lens to view human behavior. You know,
Speaker 2: we're taking these messy, chaotic, totally real world situations and
Speaker 2: stripping them down to their mathematical cores to see what's
Speaker 2: actually going on beneath the surface.
Speaker 1: Yeah, and that leap, like from human chaos to mathematical
Speaker 1: certainty is just profound. But to get to that proof
Speaker 1: about why the first wins, we first need to look
Speaker 1: at a classic problem that perfectly illustrates unregulated human self interest.
Speaker 2: Right, the classic stable matching problem.
Speaker 1: Exactly, originally posed by two mathematical economists, David Gale and
Speaker 1: Lord Shapeley, way back in nineteen sixty two. And the
Speaker 1: textbook sets this up with a highly relatable summer internship scenario.
Speaker 2: Oh yeah, the internship one. So picture your friend Roj
Speaker 2: rog goes through this grueling interview process and finally accepts
Speaker 2: a summer job at this massive, totally stable telecom company
Speaker 2: called Cleanet.
Speaker 1: The ink is dry, he set, right.
Speaker 2: You'd think so. But a few days later, a hip
Speaker 2: little startup called web Exodus, who had completely ghosted him previously,
Speaker 2: suddenly calls up and offers him the position, and.
Speaker 1: Roj obviously prefers the startup. I mean it's a cooler vibe,
Speaker 1: probably better long term prospects, So, acting in his own
Speaker 1: pure self interest, he just retracts his acceptance from Clunet
Speaker 1: and bails to go to web Exodus exactly.
Speaker 2: And now Clunet is down an intern so they panic.
Speaker 2: They go to their weightlist and call another applicant, but
Speaker 2: that applicant has already accepted an offer at a third company, Babblesoft.
Speaker 1: Right, so that person bails on Babbelsoft to go to Clunet.
Speaker 1: The disruption just cascades through the entire industry.
Speaker 2: Yeah, and the instability flows in the other direction as well.
Speaker 2: Like I suppose a student named Chelsea is scheduled to
Speaker 2: go to Baiblsoft mm hm. She hears about rog is
Speaker 2: maneuvering and decides to just call up web Exodus herself.
Speaker 2: Bold move, very bold. Yeah. She tells them she'd rather
Speaker 2: work there. Web Exodus looks at her resume, realizes they
Speaker 2: actually prefer Chelsea over one of the students they already hired,
Speaker 2: and they just ruthlessly retract an earlier offer to hire
Speaker 2: Chelsea instead.
Speaker 1: So agreements mean absolutely nothing because people are constantly looking
Speaker 1: for a side deal. Gail and Shapley basically realize that
Speaker 1: to stop the spiral, you need a system that is
Speaker 1: fundamentally like self.
Speaker 2: Enforcing a stable outcome, yeah, where.
Speaker 1: Individual self interest actually prevents any of these behind the
Speaker 1: scenes deals from happening in the first place.
Speaker 2: Right. And to study this, the economists stripped away all
Speaker 2: the complexities of the corporate world, you know, the different salaries,
Speaker 2: the unequal numbers of applicants and jobs, and they abstracted
Speaker 2: it into a classic marriage model.
Speaker 1: Okay, let's unpack this bare bones model because it's key.
Speaker 1: We have n men and n women. Each man has
Speaker 1: a ranked preference list of all the women from his
Speaker 1: absolute favorite to his least favorite, with no ties allowed, and.
Speaker 2: Each woman has a similarly ranked list of all the men.
Speaker 1: Right, So the goal is a perfect matching. Everyone gets
Speaker 1: married to exactly one person. No one is single. But
Speaker 1: the real objective here is finding a stable.
Speaker 2: Matching, and stability here is defined specifically by the absence
Speaker 2: of an instability.
Speaker 1: Which means what exactly in math terms.
Speaker 2: An instability formally exists if there's a man or a
Speaker 2: woman who are not married to each other, but who
Speaker 2: actually prefer each other over their current assigned partners.
Speaker 1: Okay, I picture an instability like a precarious house of cards,
Speaker 1: or maybe a tightly woven fabric. If even one pair
Speaker 1: of people look at each other across the room and
Speaker 1: both realize, like, hey, I'd rather be with you than
Speaker 1: the person I was assigned to, they just bypass the rules.
Speaker 2: They do. They act in their own self interest exactly.
Speaker 1: They pull that thread, run off together, and the entire
Speaker 1: fabric unravels because their abandoned partners are suddenly single, forcing
Speaker 1: them to find new matches, displacing others in an endless
Speaker 1: chain reaction.
Speaker 2: What's fascinating here is that Gail and Shapley proved mathematically
Speaker 2: that a completely stable matching, a fabric that absolutely will
Speaker 2: not unravel, always exists. I wait, always, always, for literally
Speaker 2: any set of preferences, no matter how conflicting, contradictory, or tangled,
Speaker 2: you can always pair people up so that no two
Speaker 2: people want to abandon the system for each other.
Speaker 1: Okay, but knowing a stable outcome exists hovering out there
Speaker 1: in the ether is one thing. Human preferences are impossibly complex.
Speaker 1: How do you actually find that stable state without just,
Speaker 1: I don't know, trying millions of combinations until you get lucky.
Speaker 2: Well, you use the Gail Shapley algorithm. It's a h
Speaker 2: It's a step by step mechanical process. Initially, everyone is
Speaker 2: completely free. The algorithm dictates that an unengaged man looks
Speaker 2: at his preference list and proposes to the absolute highest
Speaker 2: ranked woman. He hasn't asked yet, so.
Speaker 1: He starts right at the top of his.
Speaker 2: List, exactly. He starts at the top. If that woman
Speaker 2: is free, she accepts, and they become engaged.
Speaker 1: But the text is clear that this is only an
Speaker 1: intermediate state, right, Like they aren't fully married yet.
Speaker 2: Right. It's at the native engagement because if she receives
Speaker 2: a proposal later from a man she ranks higher than
Speaker 2: her current fiance, she immediately breaks the engagement to trade up.
Speaker 1: Ouch.
Speaker 2: Yeah, brutal for the guy. The rejected man becomes free again,
Speaker 2: moves down to the next woman on his list, and
Speaker 2: proposes to her.
Speaker 1: The mechanics are relentless. I mean, the men are constantly proposing,
Speaker 1: getting rejected, and working their way down their preference lists,
Speaker 1: so their choices are getting progressively worse.
Speaker 2: Over time yep, moving down the ranks.
Speaker 1: Meanwhile, the women are holding these tentative engagements, essentially keeping
Speaker 1: a safety option on the hook, just waiting for a
Speaker 1: better offer to come along. They break hearts to trade up,
Speaker 1: meaning their situations are getting progressively better. And this runs
Speaker 1: until no man is free, the music stops, all engagements
Speaker 1: become final marriages, and the system terminates.
Speaker 2: That's the algorithm in a nutshell.
Speaker 1: Okay, but here's where it gets really interesting to me.
Speaker 1: Looking at the sheer mechanics of that process. Logic dictates
Speaker 1: that the women must get the absolute best end of
Speaker 1: the deal. I mean they're holding all the cards. You'd
Speaker 1: think so yah, right, They sit back, hold onto a
Speaker 1: safety net, and continually trade up for better offers. The
Speaker 1: men are facing constant brutal rejection and moving further and
Speaker 1: further down their lists. So clearly the women have all
Speaker 1: the power here.
Speaker 2: That is the intuitive leap almost everyone makes. It completely
Speaker 2: looks like the women are in the power position, but
Speaker 2: in a massive twist of mathematical irony, that assumption is
Speaker 2: entirely wrong. Seriously, Yeah, the algorithm is heavily fundamentally biased
Speaker 2: in favor of the proposers in this specific model.
Speaker 1: The men, wait, how is that mathematically possible when the
Speaker 1: women are the ones doing all they're rejecting.
Speaker 2: It comes down to the difference between playing on offense
Speaker 2: and playing on defense. The textbook proves this beautifully. Remember
Speaker 2: there could be multiple valid stable matchings for any given
Speaker 2: group of people. Okay, but when you run this algorithm,
Speaker 2: the side that does the proposing is systematically testing the
Speaker 2: waters from the top down. The very first woman who
Speaker 2: accepts a man and stays with him represents the absolute
Speaker 2: highest possible match he could achieve in any stable universe.
Speaker 2: He hits the ceiling of his potential. He never even
Speaker 2: has to ask anyone lower.
Speaker 1: Oh wow. While the receiving side, the women who felt
Speaker 1: like they were in total control, are purely playing defense.
Speaker 2: Exactly. A woman might desperately want the man at the
Speaker 2: top of her list, but if he never proposes to her,
Speaker 2: she can never get him. She is entirely dependent on
Speaker 2: who approaches her door.
Speaker 1: That makes so much sense. She only gets to filter
Speaker 1: a limited, self selecting pool of candidates.
Speaker 2: Precisely by constantly try rating up among the people who
Speaker 2: do propose. She isn't achieving her dream scenario. She's just
Speaker 2: elevating herself from the absolute bottom of her potential options.
Speaker 2: The algorithm mathematically ensures that every single woman ends up
Speaker 2: with the lowest ranked man she could possibly end up
Speaker 2: with while still maintaining a stable system.
Speaker 1: So she catches the floor while the proposer hits the ceiling.
Speaker 2: You got it.
Speaker 1: That is staggering the illusion of choice. You know, the
Speaker 1: ability to sit back and reject people actually results in
Speaker 1: the worst possible stable outcome for you. The side facing
Speaker 1: constant rejection ends up with the optimal outcome. And why
Speaker 1: does this matter to you the listener? Because this exact
Speaker 1: algorithm governs massive parts of our lives.
Speaker 2: Oh totally. The National Resident Matching Program, which places thousands
Speaker 2: of medical students into hospitals every year, uses a variation.
Speaker 1: Of this School placements use this too. Understanding the math
Speaker 1: proves that being the proposer or the initiator in any
Speaker 1: system isn't just psychologically empowering, it provides a mathematically guaranteed advantage.
Speaker 2: It's a profound structural reality hidden inside a matching algorithm.
Speaker 2: But as algorithm designers know, the textbook version of the
Speaker 2: Gail Shaply model assumes a perfect vacuum like everyone ranks everyone,
Speaker 2: no one has deal breakers.
Speaker 1: But the real world is infinitely messier. What happens when, say,
Speaker 1: our friend Raj simply lacks the legal certification to work
Speaker 1: at what bex it is? Or what if two specific
Speaker 1: people absolutely refuse to be paired together under any circumstances.
Speaker 1: Doesn't the whole algorithm just break down?
Speaker 2: You'd think it might, but the text actually anticipates this
Speaker 2: by introducing the concept of forbidden pairs. The Gaale Shaply
Speaker 2: algorithm is remarkably robust. It adapts to this messy reality
Speaker 2: without breaking a sweat. How so you simply cross the
Speaker 2: forbidden pairs off the preference lists, the men only proposed
Speaker 2: to non forbidden women, the women only accept non forbidden men,
Speaker 2: and the math holds up perfectly. It still guarantees a
Speaker 2: stable matching where no two people who are legally allowed
Speaker 2: to match to abandon the system.
Speaker 1: Okay, but that leads to an even bigger human complication
Speaker 1: because humans cheat.
Speaker 2: They sure do.
Speaker 1: If the math legally mathematically guarantees that the receivers get
Speaker 1: their worst valid outcome, can a receiver just lie on
Speaker 1: her preference list to trick the algorithm into giving her
Speaker 1: a better match.
Speaker 2: Ah, so you've hit on the truthfulness dilemma, which is
Speaker 2: honestly a major headache and algorithm design. The text explores
Speaker 2: the scenario where a woman realizes she's going to get
Speaker 2: a low tier match because she's on the receiving end,
Speaker 2: so she decides to artificially swap the ranking of two
Speaker 2: low tier men on her submitted list. She essentially rejects
Speaker 2: a man she actually likes slightly better, forcing a breakup
Speaker 2: and sending him back into the proposing pool.
Speaker 1: Oh, I see the strategy. Yeah, because that rejected man
Speaker 1: is back in the pool, he bumps into another woman
Speaker 1: and proposes to her. That might cause a cascade of
Speaker 1: broken engagements across the whole system, which maybe, just maybe
Speaker 1: knocks a top tier man out of his current engagement
Speaker 1: and sends him proposing down his list until he reaches
Speaker 1: the woman who lied.
Speaker 2: You just trace the exact cascade perfectly. By lying about
Speaker 2: her preferences at the bottom of her list, she alters
Speaker 2: the entire sequence of proposals happening above her.
Speaker 1: That's wild.
Speaker 2: The textbook proves it's entirely possible for a receiver to
Speaker 2: improve their final outcome by falsely reporting their preferences, and
Speaker 2: this is a critical lesson. Algorithm designers can't just stop
Speaker 2: at the elegant math. They have to constantly analyze the
Speaker 2: boundaries of the rules, human behavior, and whether the players
Speaker 2: are economically or psychologically incentivized to lie to the system.
Speaker 1: Designing an algorithm is basically designing a game where you
Speaker 1: have to assume the players will actively try to break it.
Speaker 2: Pretty much.
Speaker 1: Yeah, so we've successfully navigated stable matching. We found a clean,
Speaker 1: efficient mechanism for a chaotic problem. But as we try
Speaker 1: to model more complex human behaviors, the math starts to
Speaker 1: really push back. The textbook zooms out here to look
Speaker 1: at the entire landscape of computational difficulty using what they
Speaker 1: frame as a staircase of five representative problems.
Speaker 2: Five problems serve as milestones for the rest of the
Speaker 2: study of algorithms, because not everything is as solvable as
Speaker 2: matching interns to jobs. Let's look at what happens when
Speaker 2: a problem goes from easily solvable to practically impossible.
Speaker 1: Okay. Step one on this staircase is interval scheduling. You
Speaker 1: have a single resource, like an expensive electron microscope, and
Speaker 1: a bunch of researchers want to reserve it for overlapping times.
Speaker 1: You want to accept the maximum number of requests. The
Speaker 1: intuitive human approach might be to pick the shortest requests first,
Speaker 1: or maybe the ones that start the earliest, right, But the.
Speaker 2: Textbook shows the most efficient way is a pure greedy algorithm.
Speaker 2: The mechanism is incredibly simple, almost myopic. You just look
Speaker 2: at the pile of requests, pick the single request that
Speaker 2: finishes the earliest and book it.
Speaker 1: Just the earliest finished time yep.
Speaker 2: Then you cross out anything that overlaps with it and
Speaker 2: pick the next available request that finishes earliest. You don't
Speaker 2: look ahead, you don't strategize, You just greedily the next
Speaker 2: finishing task for this specific problem, that simple mechanism mathematically
Speaker 2: guarantees an optimal solution every time.
Speaker 1: Okay, but that works when every request is considered equal.
Speaker 1: Let's step up the staircase to weight at interval scheduling.
Speaker 1: What if we attach money to this, Say a massive
Speaker 1: pharmaceutical company will pay ten thousand dollars to rent that
Speaker 1: microscope for the whole day, while five smaller researchers will
Speaker 1: pay one thousand dollars each for two hour slots. That
Speaker 1: changes everything, right, because the greedy rule picking the earliest
Speaker 1: finishing tasks would grab the small researchers and make five
Speaker 1: thousand dollars but completely miss the ten thousand dollars payout.
Speaker 1: The greedy mechanism just totally breaks.
Speaker 2: It fails completely because the simple rule cannot account for value.
Speaker 2: So to solve this, the text introduces a technique called
Speaker 2: dynamic programming.
Speaker 1: How does that work?
Speaker 2: The mechanism here is essentially having the algorithm keep a
Speaker 2: highly detailed scorecard. Instead of deciding blindly in the moment,
Speaker 2: it calculates the maximum possible profit for every single overlapping
Speaker 2: scenario saves those answers in a massive table and then
Speaker 2: works backward.
Speaker 1: Oh clever.
Speaker 2: Yeah. By the time it reaches the first request, it
Speaker 2: just looks at its table to trace the absolute most
Speaker 2: profitable path through the whole day. It requires more memory
Speaker 2: and calculation, but it is still highly efficient.
Speaker 1: Got it. So the third step on the staircase scals
Speaker 1: things up even more bipartite matching. We aren't just scheduling
Speaker 1: one microscope anymore. We're trying to assign dozens of professors
Speaker 1: to dozens of courses based on their specific overlapping qualifications.
Speaker 2: And for this you need a technique called network flow.
Speaker 2: The mechanism treats the connections between professors and classes like
Speaker 2: water pipes. You push water, which represents the assignments through
Speaker 2: the pipes from the professors to the classes.
Speaker 3: Okay, I can visualize that the magic of this algorithm
Speaker 3: is that if you hit a bottleneck, say you assign
Speaker 3: Professor X to calculus, but later realize Professor Y can
Speaker 3: only teach calculus, the algorithm can actively push water backward.
Speaker 2: It unassigns Professor X, re ruts them to algebra, and
Speaker 2: opens up the calculus capacity. For a professor y. It
Speaker 2: systematically builds and reroutes the optimal flow.
Speaker 1: That is genuinely brilliant. But this is where the staircase
Speaker 1: gets incredibly steep. Step four is called independence set, and
Speaker 1: I like to think of this as the dinner party.
Speaker 2: Problem, a very stressful dinner party.
Speaker 1: Extremely you want to invite as many friends as possible
Speaker 1: to a dinner party, but certain pairs of friends absolutely
Speaker 1: despise each other. You map this out by drawing a
Speaker 1: graph where your friends are dots and their conflicts are
Speaker 1: lines connecting them. You need to find the largest possible
Speaker 1: group of dots with absolutely no lines between them.
Speaker 2: This problem introduces a massive paradigm shift. Independent set belongs
Speaker 2: to a class of problems called NP complete. There is
Speaker 2: no known efficient algorithm for it. No greedy rule, no
Speaker 2: dynamic table, no flowing water.
Speaker 1: None of the tricks work.
Speaker 2: None of them. If you want to find the absolute
Speaker 2: largest complict free dinner party, you basically have to use
Speaker 2: brute force search, meticulously checking every possible combination of guests.
Speaker 2: Finding the answer is agonizingly hard.
Speaker 1: Okay, if it's so incredibly difficult to solve, why is
Speaker 1: it such a famous milestone in computer science.
Speaker 2: Because of a fascinating asymmetry. Finding the answer is practically
Speaker 2: impossible as the group gets larger, but checking the answer
Speaker 2: is trivially easy. What do you mean if I hand
Speaker 2: you a list of a hundred friends and claim here
Speaker 2: is a massive conflict free dinner party. You can just
Speaker 2: check that specific list against your conflict graph in a
Speaker 2: matter of seconds. NP complete problems are defined by this asymmetry.
Speaker 2: Finding the solution is a nightmare, but verifying a proposed
Speaker 2: solution is incredibly fast.
Speaker 1: Which brings us to the terrifying top of the staircase.
Speaker 1: Competitive facility location.
Speaker 2: This one is intense.
Speaker 1: Imagine two giant coffee chains, right, Let's call them Java
Speaker 1: Planet and quek Wakes Coffee. They're locked in a turf
Speaker 1: war taking turns opening cafes in a city, but local
Speaker 1: zoning laws state no two cafes can be adjacent. Java
Speaker 1: Planet opens a store, then Quekwigs, then Java Planet. Strategic
Speaker 1: board game, the question is does the second player have
Speaker 1: a mathematically guaranteed winning strategy to hit a specific revenue target.
Speaker 2: This problem is categorized as pspace complete. This is a
Speaker 2: whole different universe of difficulty. It is not just hard
Speaker 2: to solve. It is practically impossible to even check the answer.
Speaker 1: Because there's no single list of friends to verify. Like
Speaker 1: if you hand me the answer and say yes, quekwigs
Speaker 1: has a winning strategy, I can't just quickly check a graph.
Speaker 1: I basically have to argue with you exactly. I have
Speaker 1: to say, okay, but what if Java Planet opens on
Speaker 1: fifth Street? Then what do you do? Okay, but what
Speaker 1: if they open on sixth Street? Instead? I have to
Speaker 1: analyze a vast, endlessly branching game board of alternating future moves.
Speaker 2: The gulf between NP complete and P space complete is profound.
Speaker 2: NP is about finding a single static configuration. Psbas is
Speaker 2: about planning, game playing, and navigating a massive tree of
Speaker 2: hypothetical futures.
Speaker 1: Wow. Okay, so we keep throwing around the word efficient.
Speaker 1: We say stable matching is efficient, but finding the optimal
Speaker 1: dinner party is not. Since some of these problems are
Speaker 1: so wildly difficult, how do computer scientists actually draw the line,
Speaker 1: Like why not just use massive warehouse sized supercomputers to
Speaker 1: brute force check every possibility for the dinner party?
Speaker 2: To answer that, you really have to look at the
Speaker 2: terrifying numbers behind brute force algorithms. Let's look at an
Speaker 2: algorithm that takes n factorial steps to check every combination,
Speaker 2: and let's ground this in reality. Imagine your GPS app
Speaker 2: trying to find the fastest route home by literally checking
Speaker 2: every single combination of streets. Okay, let's say they're only
Speaker 2: thirty street segments to choose from, just thirty items.
Speaker 1: Well, thirty items doesn't sound too bad, and my phone
Speaker 1: has a really powerful processor. Let's say it can perform
Speaker 1: a million high level instructions every single second. That should
Speaker 1: burn through thirty items instantly, right.
Speaker 2: You'd think. But to check every combination of those thirty
Speaker 2: street segments using a brute force factorial algorithm, your phone's
Speaker 2: processor would take ten to the twenty fifth years to
Speaker 2: give you the route.
Speaker 1: Ten to the twenty fifth years. Wait, our entire universe
Speaker 1: is only about thirteen point eight billion years old. It
Speaker 1: hasn't even been around for ten to the tenth years exactly.
Speaker 1: My phone would be calculating that route until the stars
Speaker 1: literally burn out.
Speaker 2: That is the brute force trap. It does not matter
Speaker 2: how fast your hardware is, exponential growth will always always
Speaker 2: outrun it. This is why computer scientists define an efficient
Speaker 2: algorithm strictly is one that runs in polynomial time. We
Speaker 2: write this as big O of N to the power
Speaker 2: of D, where n is the input size and D
Speaker 2: is a constant power.
Speaker 1: So it scales gracefully.
Speaker 2: Right, If you double the number of streets on your GPS,
Speaker 2: the time it takes to find their route only slows
Speaker 2: down by a predictable constant factor, not a universe ending
Speaker 2: exponential one.
Speaker 1: I like to visualize this concept of asymptotic order of
Speaker 1: growth what computer scientists call big O notation, like looking
Speaker 1: out the window of an airplane flying high over a forest.
Speaker 2: Oh, that's a great way to put it.
Speaker 1: Yeah, when you look down from thirty thousand feet, you
Speaker 1: do not care about the exact number of leaves on
Speaker 1: a specific oak tree. You don't care if the highly
Speaker 1: specific mathematical function of an algorithm's running time is like
Speaker 1: one point six to two n squared plus three point
Speaker 1: five n plus eight. Right, the details fade away exactly.
Speaker 1: You just care about the sweeping shape of the forest.
Speaker 1: The end squared bi go notation strips away the hardware quirks,
Speaker 1: the specific processor speeds, and all those minor lower order terms.
Speaker 1: It just reveals the true absolute computational shape of a.
Speaker 2: Problem, and by analyzing the shape of that forest, we
Speaker 2: can confidently categorize our world. We can mathematically prove that
Speaker 2: pairing medical students to hospitals has an efficient polynomial shape,
Speaker 2: while seating guests at a conflict free dinner party has
Speaker 2: a fundamentally intractable exponential shape. Algorithms are really giving us
Speaker 2: the hidden blueprints of complexity.
Speaker 1: We have journeyed from the chaotic, highly emotional drama of
Speaker 1: job hunting and dating all the way to the absolute
Speaker 1: limits of computational physics. Algorithms are clearly not just cold
Speaker 1: lines of code humming on a server somewhere.
Speaker 2: No, they're a universal language. They give us a rigorous
Speaker 2: framework to find the mathematically clean core buried inside the
Speaker 2: messiest human problems.
Speaker 1: They reveal the invisible mechanics of the systems we navigate
Speaker 1: every single day, exposing biases and structural realities we might
Speaker 1: otherwise never perceive. Absolutely and on that note, I want
Speaker 1: to leave you the listener with a final thought to
Speaker 1: maul over. Think back to our deep dive into the
Speaker 1: gale shaply unfairness proof. We established the mechanism an entirely
Speaker 1: impartial algorithm mathematically guarantees that the side making the proposals,
Speaker 1: the side on offense, ends up with their absolute best
Speaker 1: possible outcome, while the side on defense catches the floor.
Speaker 2: The math doesn't lie.
Speaker 1: It does it. So look around you. If that is
Speaker 1: an inescapable law of mathematics, how many of our real
Speaker 1: world societal systems, our economic markets, our dating norms, and
Speaker 1: our daily human interactions are quietly stacked in favor of
Speaker 1: the initiator simply because of the math.
Speaker 2: It radically changes how you view the value of taking
Speaker 2: the offensive and making the first move, doesn't it?
Speaker 1: It really does. Thank you for joining us on this
Speaker 1: deep dive. Go out there and be the proposer.