Is There An Actual Case for Cyberpunk’s Delay?

Cyperpunk 2077 launched and it turns out the PS4 and Xbox One versions of the game were riddled with bugs. This has lead to an avalanche of omniscient pundits declaring “I told ya so!”. My personally favorite roast in this Miyamoto meme.

Rushing development feeds into narratives around greedy firms. “If only they didn’t want so much money!” Much of this banter is comprised of cheap shots devoid of making real claims about what CDPR should have done. Should the game have been delayed an additional 4 months? 6 months? And if so, why? If the board really didn’t understand the scope of the bugs then the question turns to the organizational design CDPR. What organizational breakdowns led the lack of information the board had about the bugs in game. Were QA leaders not empowered to speak up or not trusted?

These are much tougher questions to answer. After all, as Pixar is fond of saying, “[Games] don’t get finished, they just get released”. The key question is when to release. There will always be bugs and there will always be new features to add. Ultimately, release timing is a cost/benefit decision. Relative to the additional development cost what increase in sales would we expect from a delay? Do we have ever higher margins from a 4 or 6 month delay? To be clear, Cyberpunk is already outselling all other CDPR games, hardley “one of the most visible disasters in the history of video games“. What further increase would analysts expect with what additional delay?

Why Do Only Product Managers Write About Games?

I recently came across this Tweet:

The article’s writer, Ran Mo, is a Lead Product Manager at EA according to his Linkedin while the quote Tweeter is a fellow product manager. Dive a bit deeper and the retweets are all from fellow VCs or product people (guess there’s something to this).

This isn’t exactly uncommon. The Deconstructor of Fun podcast is hosted by three Product Managers and guests frequently come from a similar ilk.  A scroll through the last 20 DoF blog boosts a breakdown dominated by PMs.

DoF Posts by Job Discipline

Games are at the cleavage of art and science, so why are PMs the only ones with something to say about it? The alternative voices we do have, Eric Seufert (UA), Alexandre Macmillan (Analytics), and Javier Barnes (Design), only take a couple of sentences of digestion to realize the dramatically different way they frame and discuss problems. Their pieces tend to have more backbone or a strong theory that underlies an empirical observation. I’m a fan of this approach.

PMs are driven by their social caste, mainly moving up it. Networking is crucial to this, an insight that seems to go over the head of analysts and designers (at our own peril). The PM hierarchy is reflected in the “up or out mentality” re-enforced at tech and gaming firms. A scroll through PM Linkedin and you’ll see the following ladder:

None of these motivations discredit, in any way, the strength of the ideas expressed by PMs. Or the fact they actually take the time to express them. But it does help explain why they can feel hollow at times, trying to fit a socio-political mold rather than a genuine expression. This is reflected in how many game PMs will depart for higher paying tech PM jobs in the Valley or to fellow gaming firms for title bumps. And there’s nothing necessarily wrong with that.

If I had a plea, it would be for all game disciplines to write vigorously. Write everything you know to be true and let’s hash it out. The game craft is too important to be dominated by one discipline. We should all be thinking hard about these problems.

Why Do FPS Players Like Small Maps?

It’s the incentives, stupid.

Players want to unlock content and the most efficient way to do so is to maximize how many FPS games control progression speed: SPM or score per minute. Score is usually a formula composed of objectives and kills. The key is that it’s uncapped: there’s not a fixed amount of XP up for grabs in a given match or time played (this would be a better design). The formula implies that the more “action” in a given minute of gameplay then the more score per given unit of time and the faster a player will progress. Small maps excel at encouraging this – there’s a short amount of time before you bump into an enemy or objective.

FPS players like small maps because they function as costless XP boosts. Nuketown will be making its 5th appearance in the CoD title with Cold War.

A Quick Refresher

Pricing is tough to get right. Ideally, we’d like to select a given price such that revenue is maximized (consider revenue as profit), holding all else constant.

In the above example, we consider a single price $$P$$ but we can expand this to consider the set of prices or price set that define a game or service $$P_s:{\{P_1, P_2…P_x}\}$$ Again, we want to pick the set of prices that max revenue.
Many goods or services operate under a single fixed price. For instance, a book might cost $18. Everyone who values the book$18 and above purchases it. $$revenue = n * P$$ where $$n$$ is the number of readers who value it $18 and above and $$P$$ is price. Simple enough right? Let's add consumer surplus to the story. In the example below, this is the shaded yellow area. Some readers value the book at$30 and thus are the most profitable in the transaction ($30 -$18 = $12 economic profit). The other readers made out, but perhaps not as well. But what if there was a way to charge the reader who valued the book at$30 exactly $30 and the reader who value the book at$18 exactly $18? While we could raise the price of the book to$30, we'd lose out on the $18 reader. The problem of price discrimination, the one described above, is fundamental to understanding entertainment business models. Our Case The consumer surplus model struggles to capture time. After paying a fixed price for the book, the reader chooses to consume it (is anyone surprised?). The reader continues to accrue utility from this consumption - the enjoyment of reading the book outweighs other activities. At some point (but not always), the accrued utility outweighs the initial cost. Standard Utility Curve (Reading a Book) It's import to note: the reader is not "in the hole" when we see red in the above graph. The book is a sunk cost, the reader should only consume it if doing so is better then engaging in other activities. But if we extend this graph to include more Time the curve kinks immensely. Utility Curves: Books There are incredibly steep diminishing returns to reading a book a second time. It's sort of boring. This is why book rentals (libraries) and book reselling are so popular. Why pay a fixed price for what is usually a single-use item? This rings true for movies as well. Of all films you've watched, what percent have you seen a second or third time? 1%? 5%? Subscriptions and rentals make sense for this form of entertainment. But what if instead off flattening the utility curve grew? Enter gaming. Utility Curves: Games & Books/Movies Games have a unique resistance to diminishing returns. As described in Part III: PvP environments [in games] necessitate strategies that evolve in an evolutionary process. This means equilibrium in PvP environments is constantly reshuffled with each balance change; the search for dominant strategies in an ever shifting equilibrium is the game itself. The strategic evolutionary process is a near limitless piece of content to consume. MB = MC The efficiency of any monetization or pricing system is the degree with which it can correlate marginal cost (MC) to marginal benefit (MB). In the above examples, price was fixed. This made sense given that the utility curves flattened out. But the more the curve refuses to flatten, the most discorrelated MC to MB becomes as Time continues. This gets us to the emergence of DLC and MTX. Players were playing PvP titles for hundreds, if not thousands of hours. MC failed to catch-up. DLC map packs like those in Call of Duty and Battlefield helped MC catch-up (and grow MB!) in fixed intervals but the correlation was still weak as Time persisted. MTX solved for the explosion in the marginal benefit multiplayer games were providing. Unlimited or greatly exaggerated spend caps allowed players to spend to a closer to their MB curves they were previously able to do so. Utility/Price Curve: MTX & F2P Games Software as a service (Saas) is able to generate similar growing utility, but they only charged a fixed price in recurring intervals. Again, this suggests that subscriptions might make sense for games. Games, however, generate even more heterogeneous LTU (lifetime utility) then do many SaaS products. This suggests subscriptions are better than fixed prices in correlating MB and MC, but weaker then MTX systems. We can model the heterogeneity as such: As we consider the total Lifetime Utility generated by a standard good or game, we add up an individual's LTU from lowest to highest LTU. If everyone valued a standard good the same, LTU would be linear. If a few players valued a game at a relativity extreme LTU you would see a bowed curve - the high LTUs skyrocket total LTU as they are added. Look familiar? This is exactly how observe LTVs in F2P games. Does Tech leave too much LTU on the Table? But that's not to say all non-video games have a linear Total LTU curve. Clearly, some users value say, Zoom, more than others. As Zoom usage increases, LTU does as well, but price does not. Zoom therefore fails to capture a great deal of LTU from high usage customers. MTX theory could offer a hand. Zoom does offer tiered pricing for organizations with a higher price charged for additional features, but this doesn't capture the high LTU users within an organization. Perhaps Zoom should gets in the cosmetics business - backgrounds were an amazing opportunity that never got capitalized on. Even at the organizational offering level, Zoom could create an additional tier that offered additional features to high usage customers in the tier. Multi-track cloud recording, for instance, generates incredible value for Podcasters but costs nothing additional. The dramatically different value propositions of standard goods and games necessitate different monetization schemes. This makes applying the monetization of tech companies less applicable to gaming, but perhaps SaaS as a service firms have something to learn from games. Gaming Companies Aren’t Tech Companies! Game Companies Aren’t Tech Companies Part III: The Content Problem So maybe game companies aren’t tech companies. But as much as game companies seem to borrow from tech firms there’s even more to be said for the opposite. If Netflix, a subscription service with over 10,000 movies and TV shows, has its biggest competitor in a single game, Fortnite, then perhaps there’s more for tech to learn from games. And how games deal with The Content Problem is it’s defining characteristic. Of all forms of entertainment, games present the most compelling answer to the problem. The Content Problem The fundamental axiom of economics is that there are unlimited needs and wants and only limited means to fulfill them. The parallel for entertainment might consider that core content demand nearly always outstrips supply. For example, large swaths of Game of Thrones and Harry Potter fans are underserved by a couple books, movies and TV seasons. Executives try to fill the void with licensing: Harry Potter backpacks, Game of Thrones beer, etc. But filling the core content demands are impossible: it takes far more than 1 hour to produce 1 hour of Game of Thrones while the same it not true for games. Consider the following: The content consumed in a game like Overwatch or Clash Royale is the pursuit of strategy equilibrium and/or mastery of mechanics. A new unit in Clash Royale changes how players organize their decks, even if they don’t use the unit directly (they must counter it). This can provide hundreds of new hours of content to consume relative to the near 1 man-week of labor to produce the new unit. Therefore, the marginal content output of a given member of the 16 person (!) Clash Royale team is astronomical. The genius of PvP (Player v Player) environments is how they necessitate the emergence of a meta-game. In mathematics, Player vs Environment (PvE) resembles the field of optimization where strategies are static – one and done. PvP environments, however, resemble game theory models where it has been shown strategies evolve in an evolutionary process. This means equilibrium in PvP environments is constantly reshuffled with each balance change; the search for dominant strategies in an ever shifting equilibrium is the game itself. The marginal product of labor for a given game developer completely outclasses a given producer on a movie or TV show by virtue of the medium, not the individual. Unlike games where assets are infinitely replicable, movies and TV face fixed constraints: Emilia Clarke or David Benioff can only be in a single place at a given time. They must also eat, sleep, socialize (sigh). Meanwhile, Captain Price faces no such constraints. There’s no more Game of Thrones to consume after the last episode cuts to black while there’s always another hour of Fortnite to play. How can Netflix and others adapt to the reality of these mediums? The most straightforward strategy is a content arms race. Netflix continues to spend over$17B a year on original content while scooping up oodles of back catalog content. Of course, viewers must be interested in this content for it to be “effective” and the recommendation engine plays a strong role in this. But the the last episode of Stranger Things is just that, the reco engine cannot find fill the void while operating on the same indifference curve. The “more bodies” strategy to solving the content problem is expensive to execute and as we’ll see in part 4, struggles to achieve Marginal Cost = Marginal Benefit.

Reality TV is a response: less writers, editors, and CG needed to produce a given hour of content. Shows like The Amazing Race, Big Brother and Survivor can do 20+ seasons of 22+ episodes while Game of Thrones struggles with 7 seasons of 10 episodes despite having so many more crew members. Netflix’s speed of investment here is breathtaking. But the addressable audience is more limited in scope then traditional dramas. Netflix needs a bolder evolution to combat games: TV-as-a-service.

The forgotten genre of soap opera TV provides a near perfect blueprint. For those unfamiliar, soap operas are near year-round weekly serialized television shows. The unrelenting pace has resulted in popular series like General Hospital having 14,000+ episodes over 57 years. Netflix needs to heavily invest in moving shows to a similar formats: year-round production with weekly releases. There’s always another piece of content to consume right around the corner while the back-catalog for a given show is continually expanding for newcomers. In many ways, this mirrors match-3 level production. The number one reason why players churn from match-3 is a lack of new levels and a quick glance at community pages confirms this.

King mitigates this increasing difficulty for example. This increases the time to completion but could also result in churn. Reality TV faces no such option. Another strategy is also possible however: branching narratives.

Increasingly, Hollywood is shooting movies back-to-back. It’s cheaper to continue production rather then stop and go. Why not do something similar to produce more content? In this case, shoot multiple perspectives in a given series simultaneously. Lord of the Rings production operated in a similar way with two production crews, but with a singular end product. Game of Thrones also operated in this way from a production standpoint, but again the end product was single episode. Why not dedicate an hour to each perspective? This easily multiplies a 10 episode season to 30 while holding down cost. Netflix can’t solve The Content Problem, but it can mitigate it.

Interestingly, Youtube has solved most of this problem via a two-sided marketplace. The sheer smattering of volume helps the supply-side problem even if a particular creator has a finite number of video (remember, you can still play a given game for an unlimited amount of time without “running out” of content). Youtube has encouraged users to subscribe to many different creators so regular release cadence is also accounted for as well.

Diminishing returns for linear content are extremely steep, few users will watch a film or movie more than once. Deepening the LTV of a viewer primarily come through more linear content: an extremely expensive proposition. To compete with games, TV and movies need far more supply. If technology and business models can really change creative product rather than be a vehicle for it, now has never been a better time to explore changes in storytelling.

Part IV

Game Companies Aren’t Tech Companies Part II: Platform Power (or Lack Thereof)

Part I

Ran Mo retraces the incredible fast follow of the auto-chess genre. Before you could blink an eye, what was a mod in Dota 2 (which itself was a mod) had spun off into three incarnations. The first was from the mod maker, Drodo Studios’ plainly labeled Auto Chess, the second was Valve’s Underlords, and the third was Riot’s Team Fight Tactics. It’s not clear any of them have done “well”. Certainly none of them are making any real money as there’s little to purchase amongst any of them.

Valve’s Underlords PSU Declines

But if there is a leader it’s Riot’s Team Fight Tactics (TFT) by virtue of content cadence (from a player count perspective it’s unclear if TFT is doing something meaningful). How do we explain the last entrant now leading the pack? Ran seems to argue for the power of the platform:

“Underneath the hood of League of Legends is a deep set of systems and tools: messaging and voice chat systems, social systems, matchmaking, internal development tools, event systems, cloud-based server infrastructure, post-game data analytics, and so on. These tools enhanced play experiences and supported the continuous release of contents, events, and updates.”

Ran goes farther, explaining that “We’re likely entering a new wave of consolidation today, with new moats centering on live-service ecosystems […]”

I could not disagree more. Not simply on Riot’s platform power, but the wider value of game platforms all together. Game platforms have, and will always be, valueless: there is far too much game specificity and velocity for meaningful platform features to scale. “[…] messaging and voice chat systems, social systems, matchmaking, internal development tools”? While standard and required for live-service, they are not particularly “deep”. If these features are to form a moat then it’s the equivalent of a kiddie pool defending Versailles. The ever growing number of PC game launchers all of whom do these exact things suggests as much. The primary value of game “platforms” is discovery and perhaps security.

The deeper platform features of Steam (Marketplace and Workshop) have failed to see widespread adoption. Other platforms haven’t even tried building anything beyond a friends list and chat. None of them can justify a 30% cut, and for many games “multi-tentating” or listing on multiple platforms is the clear revenue maximizing strategy. Of the top 20 Steam games, only 35% non-Valve games use either Marketplace or Workshop. Furthermore, 59% multi-tenant or are on other PC launchers. Expect this to increase as Steam degrades as the sole game discovery launcher. Activision realized this long ago when they shifted Call of Duty to Blizzard.net and away from Steam. Blizzard titles anchor the launcher with eyeballs, but do little feature work. It’s 2020 and Blizzard still region locks many titles, meaning switching from NA to EU servers entails the loss of all progression for some titles.

Other firms take game platforms to eat at certain parts of the game “supply” chain. In addition to a given piece of technology working for games, these firms hope the tech will scale outside of games as well. This is an executive fetish that rarely, if ever, pans out. Machine Zone (MZ) is the latest failed entrant into “but we’re really a tech company”. Both Cognant, its internal ad-buying platform and Satori, its cloud platform, went up in flames. MZ ended up selling to AppLovin at 10% of it’s peak value. The jury’s out if Improbable, the latest “but we’re really a tech company” can dig out of $85M losses. A bigger hope is Unity, but even then only 8% of the 716 customers to spend$100,000 or more have been non-gaming firms. Epic’s PR team was out in full force trouting The Mandalorian using Unreal, but surely this a mingy share of Epic’s revenue. Games have similar challenges to tech firms, but tech firms rarely have similar challenges to games (all squares are rectangles but not all rectangles are squares). A common theme of this blog, reiterated in this series, is that games are distinct and unique medium. The faster we embrace this, the faster the industry can evolve.

While game companies may never be tech companies there may be a small place for game technology companies. Unreal and Unity are real and are here to stay – they solve difficult and prevalent problems faced by game makers. If the games industry grows, the total addressable market for game tech solution grows as well. Is there room for more game technology companies beyond Unreal and Unity? The previously mentioned Improbable seems to think so, but little additional evidence exists.

There’s more to be said for a given game as a platform, but even then the evidence is scant. Publishers haven’t been able retain the value of extensions within a given title (Auto Chess splitting from DOTA 2) or outside of it (MOBAs splitting from Warcraft III and Heroes of the Storm). The simple ability for players to build content within games hasn’t exactly accelerated either. If anything modding has become harder not easier since it’s assent in the early 90s. Can we think of a single major 2020 release to embrace mods? Much was made of Halo 3’s Forge but we’ve seen few copycats since its release in 2007. If we consider a corollary to Bill Gates’ definition of a platform it might go something like this: “A game is a game platform when the playtime of platform content eclipses the main game.” In this view there hasn’t been a modern game as a platform. A softer version might consider a game as a platform when “the playtime of additional modes eclipses the main game”. Despite a much lower bar, it’s not clear that Fortnite, with its concerts and social spaces, has reached it either.

We’re left with Roblox, a true game platform. Roblox provides valuable tools that make it easy for developers to create compelling games as well as providing for discovery. But the jury is out to consider this the future of games, especially with it’s elder sibling, Manticore, is going nowhere fast.

RPGs vary from CCGs which vary from FPSes. FPSes might need to solve for 64 player servers, while CCGs may need transfer markets and RPGs need deep customization systems. Central platform features haven’t been able to adapt to the specificity of particular game design (and the speed at which it changes). This specificity of these challenges shrinks the total addressable market for the tech solutions game firms have devised. As a result, we’ve rarely seen game companies make a successful transition to a tech firm.

Part III

Game Companies Aren’t Tech Companies Part I: Every Game has Networking Effects and They Don’t Count for Shit

Ran Mo and Joseph Kim (@jokim1) argue for looking at games from a Silicon Valley perspective. The usual three make an appearance: moats, networking effects and platforms. And while I thought it had died out after the launch of Halo 3, there continues to exist an inferiority complex amongst game makers. Games never seem to get the mainstream or broader tech circle legitimacy many think they deserve. Despite operating in the Valley and major tech hubs, game companies don’t reach the crazy evaluations of FAANG (ATVI has a market cap of $63B, while Facebook sits at$215B). Furthermore, public intellectuals like Tyler Cowen or Ben Thompson rarely discuss games as a share of their commentary (not the case with Matthew Ball – but maybe he drinks too much kool-aid). Even internally, game firms seem to be chasing subscriptions off the heels of Netflix and Spotify.

The fundamental problem comes from not respecting or understanding games as a distinct and unique medium separate from linear content. In many ways, Kim’s piece wants to make this point, but doesn’t go for the kill by the end of the article. How to Build the Amazon of Game Companies mainly describes why game companies won’t be Amazon.

I’m writing a two-sided series to address and expound on this. In two parts (networking effects & platform power), I’ll examine why game companies fall short of traditional tech companies. Then, with another two parts, I’ll address what tech companies have to learn from games (the content problem & MC = MB).

Networking Effects

Networking effects describe the positive externalities from the n+1 user to a service. Every time someone joins Facebook, the service increases in value as others can interact with that person. Gyms, for instance, work in the opposite direction: every member who joins a gym occupies a fixed amount of capital and decreases the value to each other member.

Fawning over networking effects comes from the path dependency inherent in the model: more users leads to more users. What VC doesn’t want self-perpetuating growth? But as Margolis and Liebowitz argued during the Microsoft case:

The logic underlying path dependence is seductive but incomplete. Although these simple numerical and algebraic examples appear both logically sound and structurally uncontroversial, these examples actually entail severe restrictions. […] Given that the theoretical claim that can be made for path dependency should be understood as only a demonstration of possibility, the case for path dependence becomes an empirical one.

It’s not that networking effects aren’t real, but they’re not as powerful as first made out to be. After all, networking effects couldn’t save Friendster or Myspace and as we’ll see they mean little for particular games.

At a certain scale, diminishing returns decay positive network effects to zero. The 2nd user who joined Facebook was far more beneficial to the 1st user then the 100th million user who joined. While not directly comparable, Google’s Chief Economist, Hal Varian, makes this point in regards to the predictive accuracy of models with additional data.

And we can create a similar arbitrary model for a particular game: diminishing network effect value for the marginal user that inevitably results in an asymptotic total network effect value.

Not all games face the same curve. A game like Hearthstone, with 1v1 play, has far less to gain from an additional user then say, League of Legends, which has 5v5 and many ranked segments. More users reduce matchmaking times, potential latency, and thicken skill distribution (higher P you’ll be matched against similar skill). The less segments (modes, ranks etc), the less powerful networking effects are and the quicker the marginal value curve depresses. Cross-play doubled down on this by removing platform segmentation. But even for League the networking effect power is infinitesimally small at scale, the big gains are eaten with a relatively low user count.

Games don’t have the “sticky” elements of networking effects. Synchronous consumption is another example. In any real-time game, a network effect is delivered when you play with friends. Whereas something like Instagram Stories are consumed whenever the user pleases. It’s not clear that having a friend play the same game I do is beneficial unless we play at the same time.

Schelling’s Nobel Prize winning work on tipping is a more apt model for describing many games today. Consider a group of players all playing the same game. These players are partially driven to play this particular game to be “in the know” or a part of the pop-culture conversation. Each of these players has a given threshold for defecting to a new game based on the share of public conversation consumed by the game. For instance, some might defect once the game goes from 100% of the public conversion to 99%, while more might leave if the share declines from 90% to 80%. Of course, the inverse is true as well. Some might join a game if it’s share of public conversation goes from 0% to 1% and even more would join if it went from say, 20% to 30%. A new game release can set off this “tipping” chain-reaction in players. Look no further than the migration of Fortnite players into Warzone. The power of tipping is as strong as “public conversation” is as a player motivation. Unfortunately for developers this means instability in long-run capitalization of viral game hits.

I’ve avoided addressing two-sided marketplaces as they’ll more neatly fit into the next part: platform power (or lack thereof).

Part II

Can We Get Players to Tell Us Their LTV?

Eric Suffert acutely describes the dangers of extending payback windows. At every t+1 the accuracy of LTV declines while the variance in cohort profitability increases. LTV, however, is not an exogenous variable and clever design can incentivize players into revealing their long-run time horizons within a game.

Consider the design of a many subscriptions: you can pay a lower annual fee or a higher month-to-month fee. If you’re uncertain about the subscription, then the month-to-month is more economical while if you have more certainty then the annual fee makes more sense. The choice is a huge predictor of retention: annual users are far more likely to retain then month-to-month users. The mere inclusion of this annual/month-to-month choice gives users the opportunity self-segment into more predictable cohorts. Why can’t we use the same mechanics in game design to create more predictable LTVs?

Consider two possible goods for purchase via gems in Clash of Clans: a builder or gold. The builder increases the long-run growth rate of gold while the gold itself is a temporary boost in short-run capital stock. In layman’s terms: spending 100 gems on a builder might net you 200 gold today and 1,000 gold by D30 while spending 100 gems directly on gold may only yield 700 Gold today and 0 gold by D30. The builder is an annuity that pays dividends every period, the longer a player’s time horizon the more valuable the annuity.

Players who expect to have a long time horizon in a given title have an enormous incentive to purchase “investment” goods or goods that pay dividends overtime (battle passes similar to some degree). Not doing so results in a increasing opportunity cost penalty every period due to lost compounding growth.

F2P has experimented with direct daily annuities of hard currency. They offer players a discount over the standard IAP packs but must pay upfront to receive a daily allowance. Instead of a 30-day pass, why not ramp to a quarterly or bi-annual pass? Doing so would make LTVs more predictable early in given player’s lifecycle.

Kantian Ethics For Game Ads and Beyond is Probably a Good Idea

The ASA has banned misleading ads from Playrix’s Gardenscapes. Running the same creative in user acquisition hits diminishing returns fairly quickly as the creative “clears the market” for users attracted to that creative. To broaden appeal, why not simply advertise the game as existing in a entirely different genre? This opens up a whole new segment and drops CPIs significantly. Of course, advertising gameplay that doesn’t exist surely means these users will exhibit extremely poor KPIs. However, there’s a broader implication to these ads and one that harms the entire game industry.

The harm is described in Akerlof’s Nobel Prize winning paper on car lemons:

If users start to have an expectation that a given game ad does not truthly describe the game in question then they’ll be less likely to click. This means higher UA costs even if your firm does not engage in these type of ads.

A similar problem is starting to creep up in PvP games. Developers have started to be confronted with the uncomfortable reality that PvP is a zero-sum game: for someone to win, someone else has to lose. And of course, when players cannot progress they churn. Supercell has heavily introduced bots in Clash Royale as a response.

Of course, players cannot identify if they are indeed playing a bot or a real player. This steals one of the compelling aspects, if not the compelling aspect, of PvP away from players: outsmarting another individual. But like the lemon problem above, if this trend continues then players will start to question if they’ve dominated a real opponent even if the particular game doesn’t use bots. Games that use unmarked bots start an industry expectation that diminishes the experience for all.

There’s a good moral rule here to help us and it’s from an 18th century philosopher called Immanuel Kant. Kant advocates for something called the Categorical Imperative. This claims that if we were to universalize a given action and it would result in a “contradiction” then that action is immoral. Consider lying: if everyone were to lie then the world would not function, therefore lying is immoral. Or consider being lazy: if everyone were lazy then nothing would get done. It’s a no-holds-barred approach to consider moral action, but thankfully we’ll use it a much more narrow scope.

If unmarked bots were to be universalized, PvP games would be irrevocably harmed. If misleading ads were to be universalized, then players would stop clicking on them all together. Both of these situations violate the Categorical Imperative and align with outcomes that benefit the entire industry. The Categorical Imperative makes for a simple and rule based approach to consider the “greyer” parts of developer action.

The Collective-Action Problem of F2P Clans Remains Unsolved

There’s a compelling aspect to achieving group oriented goals: being apart of something larger than yourself. Lots of F2P developers harp on the importance of social features. Yet the social experience in many games is abysmal. Lots of teammates or clanmates don’t seem interested in participating instead preferring to “free-ride”, putting forward little effort but getting the fruits of the team reward. Mancur Olson’s foundational work, The Logic of Collective Action, describes how this problem manifests in the public sphere (sometimes literally in the case of electric scooters). Game designers have a much easier time aligning individual and clan incentives than public officials yet they sometimes miss easy wins. How can we make the clan experience better then it might otherwise be?

In Clash Royale, clans advance a boat against rival clans. Advancing the boat depends on individual clanmates playing games everyday (and winning). The more clanmates play consistently, the more the boat advances and the better the rewards the clan will receive. But for many clanmates playing everyday requires a great of effort, why not let others earn the rewards for you?

The problem is severe in Battlefield where “PTFO” or “Play the Fucking Objective” is standard nomenclature. Players often won’t engage in activities that benefit the team (capturing flags), instead preferring to pursue their own objectives (generally: shoot players as fast as possible).

A given player faces two potential payoff schedules when considering to allocate effort to the clan. There’s the expected payoff with no effort (the probability that the clan/team will win if the given player did nothing) as well the probability that the clan will win if the player puts forth effort. We can model this as such:

$\mathit{expected\ payoff\ from\ effort_i} = {P(\mathit{winning} | \mathit{effort_i}) \ *R}$

where

$P(\mathit{winning} | \mathit{effort_i})$

is the probability of winning the clan event given give the effort of a given player or rather the additive probability of this given player participating.

While R is the reward from winning.

Of course if $$P(\mathit{winning}| \mathit{effort_i}) = P(\mathit{winning})$$ or the given player cannot sufficiently contribute to the probability of the clan winning then there’s zero incentive for them to put forth effort. Why bother?

This problem exacerbates as team size grows: the efficacy of a given player varies inversely with the number of teammates. This makes intuitive sense: in Battlefield, a player in 2 versus 2 match has a greater impact on the outcome then a player in a 32 versus 32 player match. The incentive to free-ride rises as the number of teammates or clanmates rises. Weakness hides in numbers.

We’ve also ignored the game-theory dynamics of this problem for simplicity, but it’s worth mentioning. If I know my other teammates are not going to put forth effort, why should I? This leads to Nash equilibriums where clans have almost no activity.

How can we overcome the free-rider problem and ensure that all teammates put forth effort? The highest cost-benefit feature is simply better monitoring tools. In many clan or team based games, clan leaders face asymmetric information: they simply can’t identify the players that do not put forth effort. A simple measure of activity (last login) or games played in the last week goes a long way to kicking out free-riders. We might also consider a joint-production function. In Battlefield or Clash Royale each player would receive a score based on their effort or contribution to team advancement, if the team wins they receive a multiplier on this score. Such a system would have two benefits: it would more closely align individual effort with individual outcome (reap what you sow), and it would increase the benefit for high performing clan members to engage in monitoring. For example, a high performing member might have $20 in contributions with a 2x multiplier or$40 for winning compared to a low performing member with $5 in contributions and therefore$10 for winning. In real terms, the high performing member has an even greater incentive to encourage low performers to put forth effort.

There’s a lot to be said for social shaming as well. While it hasn’t been effective for zero effort participants, there’s evidence it might help players on the margin. A push notification demonstrating that your clans needs you or perhaps better yet, a system where your clanmates can send you push notifications is a compelling way to push players into action.

Perhaps the greatest miss I see is not in clan monitoring (kicking out free-riders), but in self-selection to begin with. Clans are generally pareto efficient for players meaning that there’s zero cost and only benefit to joining one. Players then generally look for near max-size clans as they maximize the clan’s probability of winning a reward and thus the players. Reducing search costs by recommending (or restricting) clans based on device language, location, and some measure of progression maturity makes all players better off.

It’s hard for social monetization opportunities to take-off if team based activities suck. We still have a long way to go to fix top of the funnel problems. Afterall, teamwork makes the dreamwork.