SUGGESTED VIEWING
The politics of desire
With Barry C. Smith, Zoe Strimpel, Maya Oppenheim, Kimberly McIntosh
This transformation in how we approach intimacy parallels broader trends in algorithmic influence across modern life. Algorithmic product recommendations, for instance, can lead individuals to consume less diverse items through a self-reinforcing loop where users are repeatedly exposed to similar content, limiting their exposure to new or different options. Yet nowhere is the digitisation of social life more personal than in dating. The search for love—once mediated by organic social contexts—is increasingly facilitated by algorithms.Dating apps present an ecosystem where people searching for romance are connected through algorithmic matchmaking. These algorithms play a critical role in shaping human interactions and determining which users are introduced as potential romantic partners. By analysing preferences, behaviours, and compatibility metrics, these systems profoundly influence who users meet and form relationships with. In a Bauman-esque sense, this fosters a dynamic of “liquid love,” where bonds may be easily formed but just as quickly dissolved. It also raises questions about how algorithmic matchmaking shapes human connections on and off the screen, and whether it truly facilitates meaningful relationships. The business model dilemmaOnline dating platforms promise to connect us with our ideal partners, but their primary goal is often profit maximization. Dating services rely on having a large and active user base to sustain revenue. This creates a fundamental conflict of interest: facilitating optimal matches may lead to successful, long-term relationships, reducing the number of active users and, consequently, losing potential revenue. To mitigate this, platforms are incentivized to adopt strategies that prioritise engagement over true long-term connections. For instance, algorithms might favour popular users, enhancing their visibility to maintain overall user activity, but potentially leading to biases against less popular individuals. This conflict of interest has led to user scepticism and distrust toward for-profit dating apps. Many suspect these platforms manipulate profile visibility and match suggestions to maximize profit rather than foster genuine connections. Such perceptions have contributed to declining user engagement and market value for major dating app companies. In response, some non-profit dating apps have emerged, aiming to prioritize user interests and relationship quality over financial incentives. This struggle illustrates Byung-Chul Han’s critique of commodification, which can erode genuine eros when intimacy is driven by financial motives, ultimately compromising its authenticity. The attention economyThese engagement-maximizing strategies often arise implicitly rather than through explicit design choices by system designers. The human element in algorithm design can introduce intentional and unintentional systemic biases. Decisions aimed at increasing engagement may inadvertently reinforce existing social biases or limit the diversity of potential matches. The gamification of dating apps, which incorporates game-like elements to increase engagement, can lead to addictive behaviours and superficial interactions that detract from meaningful connections. The interaction between platform incentives, user behaviour, and algorithmic design shapes these processes. They resonate with Herbert Simon’s concept of the “attention economy,” where platforms compete for users’ time and engagement as a form of currency. By examining these tensions through the lens of game theory, we can better understand how self-interested algorithms influence romantic outcomes and shape user behaviour within the platform ecosystem. In particular, we want to understand the consequences of conflicting incentives between system providers—who prioritize engagement—and users who seek meaningful relationships. The game theory of loveAlgorithmic dating can be understood as a matching problem in which individuals seek the best possible partners from a given pool of potential matches. Game theory provides a mathematical framework to examine the tension between dating apps’ goal of helping users find compatible partners and their business model, prioritizing engagement. Specifically, we can distinguish between two objectives. The first, social welfare maximization, aims to create matches that enhance user happiness, ideally fostering meaningful and lasting relationships. In contrast, self-interested matching reflects the reality of many dating platforms, where engagement metrics take precedence over user well-being.
SUGGESTED VIEWING
Sex and Love in the Digital Age
With Chris Sherwood
A key concept in game theory, the “price of anarchy,” quantifies the efficiency loss that occurs when individuals or systems act in their self-interest rather than for the collective good. In the context of dating apps, this represents the gap between an ideal matchmaking system—designed to maximize overall user happiness—and an algorithm optimized for engagement. The price of anarchy in dating platforms measures how much user well-being is sacrificed to sustain profit-driven objectives that prioritize prolonged user activity over meaningful connections. Theoretical insightsIn a recent study I co-authored, we derived theoretical limits for this efficiency loss. We developed a simple model to understand whether users would continue using the platform based on their experiences with the matching system. Imagine a user evaluating their satisfaction after each date or interaction facilitated by the platform—this perceived satisfaction, or “utility,” determines the likelihood of returning to the platform. A satisfying experience increases the probability of return, while a disappointing encounter may discourage further use. Additionally, users who find excellent matches—such as entering long-term relationships—have less reason to continue searching for partners. In the extreme case of achieving maximum utility (a perfect match), the user is unlikely to return.___In this feedback loop, the algorithm adapts to user engagement patterns: high dissatisfaction leading to departures may trigger algorithmic adjustments, whereas continued engagement despite mediocre matches reinforces the status quo.___This model captures the tension where providing high-quality matches may decrease the user base as satisfied users leave, while low-quality matches may drive users away due to dissatisfaction. However, medium-quality matches keep users engaged in finding partners, at least in the short term, striking a balance that sustains platform activity. The model indicates that efficiency loss largely depends on individual user decisions—specifically, whether users stay on the platform or leave after their matching experiences. If users frequently leave due to dissatisfaction, the platform may be forced to improve its algorithms to retain engagement. Conversely, if users continue to engage with the platform despite suboptimal matches—perhaps due to addictive design features or the hope of better outcomes—platforms have little incentive to enhance match quality. In this feedback loop, the algorithm adapts to user engagement patterns: In this feedback loop, the algorithm adapts to user engagement patterns: high dissatisfaction leading to departures may trigger algorithmic adjustments, whereas continued engagement despite mediocre matches reinforces the status quo. The role of competitionCompetition among platforms may mitigate some of these inefficiencies. When users can easily switch between dating apps, platforms are incentivized to improve match quality rather than merely promote engagement. Our theoretical analyses suggest increased competition can align the platforms’ self-interested behaviours with user interests, enhancing social efficiency. However, practical challenges such as switching costs and asymmetric information about platform effectiveness complicate this potential solution. Unfortunately, we are in a case where the online dating market is characterized by a few dominant players, which can limit the competitive pressure to improve services. For instance, Match Group owns several major dating platforms, including Tinder, Hinge, OkCupid and more apps. This consolidation reduces the incentive to innovate or address user concerns, as users may struggle to find alternatives offering substantially different experiences. Consequently, the lack of competition may allow platforms to prioritize engagement-driven strategies over enhancing match quality, further exacerbating the gap between user satisfaction and platform growth.___Users who remained on the platform with the engagement-optimized algorithm experienced satisfaction levels similar to their outside option.___ Experimental findingsTo validate our theoretical findings, we conducted experiments with human participants, avoiding assumptions about user behaviour. Before diving into the details of the experiment, it’s important to understand the concept of “outside option.” This refers to the satisfaction users could achieve if they decided to stop using the dating platform—whether that means meeting someone through friends, trying other dating apps, or simply choosing not to engage in dating at all.___The best match for you might be one you never see—because the platform wants to keep you engaged.___Our experiment revealed a striking insight: users who remained on the platform with the engagement-optimized algorithm experienced satisfaction levels similar to their outside option. That is, users’ satisfaction levels are similar to what they would have gotten if they had not used the app. Although the algorithm had no information on the outside option, it kept user satisfaction just above the threshold needed to prevent departures. This suggests that the algorithm learns from user behaviour to maximize engagement, ensuring users stay satisfied enough to return—but not necessarily because they find great matches. This fits the idea that the platform has little reason to improve match quality if users stay engaged despite poor matches. Instead, the algorithm prioritizes retention, learning from user behaviour to maintain high engagement at minimal cost. Future implicationsDating platforms promise meaningful connections, yet their profit-driven models often work against this ideal. Focused on maximizing user engagement, they tend to emphasize brief encounters over lasting bonds. This dynamic illustrates a form of liquid love—easily entered but lacking stability. By reducing romance to data points, these platforms undermine the mystery and depth of true intimacy. In fact, the best match for you might be one you never see—because the platform wants to keep you engageUnderstanding the strategic mechanics behind algorithmic matchmaking can help users navigate these systems wisely, ensuring that technology serves romance rather than exploiting it. Transparency, regulatory oversight, and user awareness are crucial in fostering an environment where platforms prioritize long-term relationship success over short-term engagement metrics. Moreover, healthy competition among dating platforms can drive better match quality and reduce inefficiencies. Finally, one must ask if the digitisation of love can ever fully preserve what makes romance human. As Bauman noted, modern life can make connections fleeting and easily broken, and Byung-Chul Han reminds us that real eros thrives on vulnerability, mystery, and authenticity. The tension between the pursuit of profits and the pursuit of love points to a deeper issue: how can we ensure that technology serves emotional well-being, rather than subverting it? As we continue to navigate the digital age, it is imperative to consider the ethical implications of algorithmic matchmaking and strive for systems that honour, rather than exploit, our profound need for genuine connection.
[[{“value”:”In the age of digital romance, dating apps have transformed love into a transactional, algorithm-driven experience. As Bauman’s concept of “liquid love” and Byung-Chul Han’s critique of consumerism suggest, this shift undermines true emotional connection. Andrés Abeliuk explores how platforms prioritize engagement over meaningful relationships, turning intimacy into a fleeting commodity. Amid this commodification, can technology ever truly foster the vulnerability and depth required for lasting love? Bauman coined the term “liquid love” to characterize modern relationships as flexible and ephemeral, with few connections feeling permanent. The digitisation of love–through dating apps–is symptomatic of this fluidity, making relationships seem disposable and the security once found in long-term partnerships is replaced by the ease of finding new options and a diminished fear of rejection. With relationships becoming commodified, reliance on algorithms can diminish our capacity for real love. Byung-Chul Han argues that true love requires a willingness to risk vulnerability, contrasting authentic eros, a deep connection with the other, against the superficial, transactional nature of love in a consumerist society. Dating apps can create connections that are easily replaced and often short-lived, hurting true emotional closeness. Together, Bauman and Han show that contemporary dating culture, propelled by technology, often deviates from meaningful emotional connection toward an endless cycle of swipes and quick judgments, turning relationships into convenience transactions.
SUGGESTED VIEWING
The politics of desire
With Barry C. Smith, Zoe Strimpel, Maya Oppenheim, Kimberly McIntosh
This transformation in how we approach intimacy parallels broader trends in algorithmic influence across modern life. Algorithmic product recommendations, for instance, can lead individuals to consume less diverse items through a self-reinforcing loop where users are repeatedly exposed to similar content, limiting their exposure to new or different options. Yet nowhere is the digitisation of social life more personal than in dating. The search for love—once mediated by organic social contexts—is increasingly facilitated by algorithms.Dating apps present an ecosystem where people searching for romance are connected through algorithmic matchmaking. These algorithms play a critical role in shaping human interactions and determining which users are introduced as potential romantic partners. By analysing preferences, behaviours, and compatibility metrics, these systems profoundly influence who users meet and form relationships with. In a Bauman-esque sense, this fosters a dynamic of “liquid love,” where bonds may be easily formed but just as quickly dissolved. It also raises questions about how algorithmic matchmaking shapes human connections on and off the screen, and whether it truly facilitates meaningful relationships. The business model dilemmaOnline dating platforms promise to connect us with our ideal partners, but their primary goal is often profit maximization. Dating services rely on having a large and active user base to sustain revenue. This creates a fundamental conflict of interest: facilitating optimal matches may lead to successful, long-term relationships, reducing the number of active users and, consequently, losing potential revenue. To mitigate this, platforms are incentivized to adopt strategies that prioritise engagement over true long-term connections. For instance, algorithms might favour popular users, enhancing their visibility to maintain overall user activity, but potentially leading to biases against less popular individuals. This conflict of interest has led to user scepticism and distrust toward for-profit dating apps. Many suspect these platforms manipulate profile visibility and match suggestions to maximize profit rather than foster genuine connections. Such perceptions have contributed to declining user engagement and market value for major dating app companies. In response, some non-profit dating apps have emerged, aiming to prioritize user interests and relationship quality over financial incentives. This struggle illustrates Byung-Chul Han’s critique of commodification, which can erode genuine eros when intimacy is driven by financial motives, ultimately compromising its authenticity. The attention economyThese engagement-maximizing strategies often arise implicitly rather than through explicit design choices by system designers. The human element in algorithm design can introduce intentional and unintentional systemic biases. Decisions aimed at increasing engagement may inadvertently reinforce existing social biases or limit the diversity of potential matches. The gamification of dating apps, which incorporates game-like elements to increase engagement, can lead to addictive behaviours and superficial interactions that detract from meaningful connections. The interaction between platform incentives, user behaviour, and algorithmic design shapes these processes. They resonate with Herbert Simon’s concept of the “attention economy,” where platforms compete for users’ time and engagement as a form of currency. By examining these tensions through the lens of game theory, we can better understand how self-interested algorithms influence romantic outcomes and shape user behaviour within the platform ecosystem. In particular, we want to understand the consequences of conflicting incentives between system providers—who prioritize engagement—and users who seek meaningful relationships. The game theory of loveAlgorithmic dating can be understood as a matching problem in which individuals seek the best possible partners from a given pool of potential matches. Game theory provides a mathematical framework to examine the tension between dating apps’ goal of helping users find compatible partners and their business model, prioritizing engagement. Specifically, we can distinguish between two objectives. The first, social welfare maximization, aims to create matches that enhance user happiness, ideally fostering meaningful and lasting relationships. In contrast, self-interested matching reflects the reality of many dating platforms, where engagement metrics take precedence over user well-being.
SUGGESTED VIEWING
Sex and Love in the Digital Age
With Chris Sherwood
A key concept in game theory, the “price of anarchy,” quantifies the efficiency loss that occurs when individuals or systems act in their self-interest rather than for the collective good. In the context of dating apps, this represents the gap between an ideal matchmaking system—designed to maximize overall user happiness—and an algorithm optimized for engagement. The price of anarchy in dating platforms measures how much user well-being is sacrificed to sustain profit-driven objectives that prioritize prolonged user activity over meaningful connections. Theoretical insightsIn a recent study I co-authored, we derived theoretical limits for this efficiency loss. We developed a simple model to understand whether users would continue using the platform based on their experiences with the matching system. Imagine a user evaluating their satisfaction after each date or interaction facilitated by the platform—this perceived satisfaction, or “utility,” determines the likelihood of returning to the platform. A satisfying experience increases the probability of return, while a disappointing encounter may discourage further use. Additionally, users who find excellent matches—such as entering long-term relationships—have less reason to continue searching for partners. In the extreme case of achieving maximum utility (a perfect match), the user is unlikely to return.___In this feedback loop, the algorithm adapts to user engagement patterns: high dissatisfaction leading to departures may trigger algorithmic adjustments, whereas continued engagement despite mediocre matches reinforces the status quo.___This model captures the tension where providing high-quality matches may decrease the user base as satisfied users leave, while low-quality matches may drive users away due to dissatisfaction. However, medium-quality matches keep users engaged in finding partners, at least in the short term, striking a balance that sustains platform activity. The model indicates that efficiency loss largely depends on individual user decisions—specifically, whether users stay on the platform or leave after their matching experiences. If users frequently leave due to dissatisfaction, the platform may be forced to improve its algorithms to retain engagement. Conversely, if users continue to engage with the platform despite suboptimal matches—perhaps due to addictive design features or the hope of better outcomes—platforms have little incentive to enhance match quality. In this feedback loop, the algorithm adapts to user engagement patterns: In this feedback loop, the algorithm adapts to user engagement patterns: high dissatisfaction leading to departures may trigger algorithmic adjustments, whereas continued engagement despite mediocre matches reinforces the status quo. The role of competitionCompetition among platforms may mitigate some of these inefficiencies. When users can easily switch between dating apps, platforms are incentivized to improve match quality rather than merely promote engagement. Our theoretical analyses suggest increased competition can align the platforms’ self-interested behaviours with user interests, enhancing social efficiency. However, practical challenges such as switching costs and asymmetric information about platform effectiveness complicate this potential solution. Unfortunately, we are in a case where the online dating market is characterized by a few dominant players, which can limit the competitive pressure to improve services. For instance, Match Group owns several major dating platforms, including Tinder, Hinge, OkCupid and more apps. This consolidation reduces the incentive to innovate or address user concerns, as users may struggle to find alternatives offering substantially different experiences. Consequently, the lack of competition may allow platforms to prioritize engagement-driven strategies over enhancing match quality, further exacerbating the gap between user satisfaction and platform growth.___Users who remained on the platform with the engagement-optimized algorithm experienced satisfaction levels similar to their outside option.___ Experimental findingsTo validate our theoretical findings, we conducted experiments with human participants, avoiding assumptions about user behaviour. Before diving into the details of the experiment, it’s important to understand the concept of “outside option.” This refers to the satisfaction users could achieve if they decided to stop using the dating platform—whether that means meeting someone through friends, trying other dating apps, or simply choosing not to engage in dating at all.___The best match for you might be one you never see—because the platform wants to keep you engaged.___Our experiment revealed a striking insight: users who remained on the platform with the engagement-optimized algorithm experienced satisfaction levels similar to their outside option. That is, users’ satisfaction levels are similar to what they would have gotten if they had not used the app. Although the algorithm had no information on the outside option, it kept user satisfaction just above the threshold needed to prevent departures. This suggests that the algorithm learns from user behaviour to maximize engagement, ensuring users stay satisfied enough to return—but not necessarily because they find great matches. This fits the idea that the platform has little reason to improve match quality if users stay engaged despite poor matches. Instead, the algorithm prioritizes retention, learning from user behaviour to maintain high engagement at minimal cost. Future implicationsDating platforms promise meaningful connections, yet their profit-driven models often work against this ideal. Focused on maximizing user engagement, they tend to emphasize brief encounters over lasting bonds. This dynamic illustrates a form of liquid love—easily entered but lacking stability. By reducing romance to data points, these platforms undermine the mystery and depth of true intimacy. In fact, the best match for you might be one you never see—because the platform wants to keep you engageUnderstanding the strategic mechanics behind algorithmic matchmaking can help users navigate these systems wisely, ensuring that technology serves romance rather than exploiting it. Transparency, regulatory oversight, and user awareness are crucial in fostering an environment where platforms prioritize long-term relationship success over short-term engagement metrics. Moreover, healthy competition among dating platforms can drive better match quality and reduce inefficiencies. Finally, one must ask if the digitisation of love can ever fully preserve what makes romance human. As Bauman noted, modern life can make connections fleeting and easily broken, and Byung-Chul Han reminds us that real eros thrives on vulnerability, mystery, and authenticity. The tension between the pursuit of profits and the pursuit of love points to a deeper issue: how can we ensure that technology serves emotional well-being, rather than subverting it? As we continue to navigate the digital age, it is imperative to consider the ethical implications of algorithmic matchmaking and strive for systems that honour, rather than exploit, our profound need for genuine connection.”}]] Read More dating, relationships, love