Why unreasonable behaviour is the top reason cited for divorce in Singapore, and adultery the least - The Star

When Marriage Becomes a system Failure: Unreasonable Behaviour and the Data Behind Singapore's Divorce Trends

Singapore's latest divorce data reveals a striking anomaly: "unreasonable behaviour" is the overwhelming grounds for divorce. While adultery-historically the headline-grabbing cause-is now the least cited. At first glance, this looks like a sociological shift. But for those of us who build and maintain complex systems, the pattern is eerily familiar. It mirrors a system where the primary failure mode isn't a catastrophic breach (adultery) but a slow degradation of core protocols (unreasonable behaviour). In software engineering, we call this a "soft error"-a gradual accumulation of friction that, left unpatched, forces a hard reset. The divorce data from Singapore isn't just a story about relationships; it's a case study in system observability, fault tolerance. And the cost of ignoring runtime anomalies.

As a developer who has spent years debugging production systems, I see "unreasonable behaviour" as the equivalent of a service-level objective (SLO) violation that has been ignored for too long. Adultery, by contrast, is a security breach-a clear, auditable event. But the data shows that most marriages in Singapore fail not because of a single, dramatic incident. But because of a pattern of behaviour that degrades the relationship's performance to the point of no return. This article will dissect the engineering parallels, explore the data pipelines behind these statistics, and argue that the real story is about how we measure, monitor. And respond to system degradation-whether in a marriage or a microservice architecture.

The Data Pipeline: How Singapore's Ministry of Social and Family Development Tracks Divorce

To understand why "unreasonable behaviour" tops the list, we first need to examine the data collection system. The Singapore Ministry of Social and Family Development (MSF) publishes annual divorce statistics based on applications filed under the Women's Charter. The data is aggregated from the Family Justice Courts, where each petition must specify the grounds for divorce. This isn't a survey; it's a legally binding classification system. The categories include adultery, unreasonable behaviour, desertion,, and and separation (with or without consent)

From an engineering perspective, this is a classification system with a hardcoded taxonomy. The taxonomies were defined decades ago, and they haven't been updated to reflect modern relationship dynamics. For instance, "unreasonable behaviour" is a catch-all that covers everything from financial irresponsibility to emotional neglect to incompatibility. In database terms, this is a VARCHAR(255) field that has become a dumping ground for every failure mode that doesn't fit neatly into the other categories. The 2024 MSF report shows that 38. 7% of divorces cited unreasonable behaviour, while adultery accounted for only 2. 3%. This isn't because adultery is rare-it is because the legal system incentivizes petitioners to choose the path of least resistance.

For a technical audience, this is reminiscent of a bug-tracking system where developers always label issues as "Type: Enhancement" because it requires less documentation than "Type: Critical. " The data is skewing the true failure profile. The real question is: what does the raw telemetry-the unsanitized logs of a relationship-look like? We can infer that many divorces involve multiple failure modes. But the system forces a single classification. This is a classic case of data loss due to schema rigidity,

Unreasonable Behaviour as a System Degradation Pattern

In distributed systems, we distinguish between fail-stop failures (the node dies) byzantine failures (the node behaves arbitrarily). Adultery is a fail-stop event-it is unambiguous, auditable,, and and typically triggers immediate actionUnreasonable behaviour is a byzantine failure: it's gradual, subjective. And often denied by the offending party. The system degrades over months or years, with each incident adding latency to the relationship's request-response cycle. Eventually, the system becomes so slow that it times out (separation) or crashes entirely (divorce).

Singapore's data suggests that most couples aren't waiting for a catastrophic event to file for divorce they're filing because the system's performance has dropped below a tolerable threshold. This aligns with the concept of error budget in site reliability engineering (SRE). Every relationship has an error budget-a finite tolerance for disappointment, neglect. Or conflict. Once the budget is exhausted, the relationship enters a "maintenance mode" that's unsustainable. The fact that 1 in 5 divorces are granted by mutual agreement (without assigning blame) further supports this thesis: both parties recognize the system is degraded. And they agree to a graceful shutdown rather than a post-mortem.

From a software architecture standpoint, this is analogous to a system that has accumulated so much technical debt that the cost of refactoring exceeds the cost of rewriting. The "unreasonable behaviour" classification is essentially a catch-all for "we have too many unresolved tickets. " The engineering lesson is clear: monitor for degradation, not just crashes. If you wait for a catastrophic failure, you have already lost the opportunity to intervene.

The Adultery Paradox: Why the Least-Cited Ground is the Most Visible

Adultery accounts for only 2. 3% of divorces in Singapore. Yet it dominates media coverage and public discourse. This is a classic availability bias in information systems. Adultery is a high-signal event-it is easy to detect, easy to prove. And generates compelling headlines, and unreasonable behaviour is low-signal, high-noiseIt requires extensive documentation, witness testimony, and subjective interpretation. The legal system is optimized for high-signal events, which is why adultery has a dedicated category, while unreasonable behaviour is a catch-all.

In cybersecurity, this is analogous to the difference between a brute-force attack (loud, visible, easy to log) and a slow, targeted data exfiltration (quiet, invisible, hard to detect). Security teams often focus on the loud events because they generate alerts. While the quiet events cause the most damage. Similarly, family law practitioners know that many couples cite "unreasonable behaviour" even when adultery occurred. Because it's easier to prove and less adversarial. The data isn't lying. But it's systematically underreporting adultery due to classification bias.

For developers building legal tech platforms, this is a critical insight. If you're designing a case management system for family law, you need to account for this classification bias. A naive system that trusts the primary classification will produce misleading analytics. You need to add multi-label classification or allow for secondary grounds, even if the law doesn't require it. Otherwise, your dashboards will tell a story that doesn't match reality.

Observability in Relationships: What Telemetry Would Reveal

Imagine if marriages had the same observability stack as a production system. You would have logs (text messages, emails), metrics (frequency of arguments, duration of silences). And traces (the sequence of events leading to a breakdown). In Singapore, the MSF data is the equivalent of a single metric-divorce rate by grounds-with no traces, no logs. And no context. It tells you the outcome, but not the root cause.

What would a real observability platform reveal? Based on qualitative studies and our own experience, the most common patterns are: communication latency (time to respond to emotional needs), resource contention (arguments over money or time), dependency failure (one partner relying too heavily on the other for emotional support). These are all measurable. For example, a study by the University of Singapore found that couples who argued about finances more than once a week were 30% more likely to divorce within five years that's a metric you can track.

From an engineering perspective, the solution isn't to build a surveillance system for marriages-that would be dystopian. The solution is to apply the principles of blameless post-mortems to relationship breakdowns, and instead of asking "who is at fault" (the legal approach), we should ask "what systemic factors contributed to the failure? " This shift from blame to analysis is exactly what SRE teams do after an incident. The data shows that Singapore's legal system is still stuck in a blame-oriented paradigm, which is why unreasonable behaviour is the top category-it is the only one that allows for a narrative of fault without requiring proof of a specific event.

The Mutual Agreement Anomaly: Graceful Shutdowns in Production

One of the most interesting data points from the MSF report is that nearly 1 in 5 divorces are granted by mutual agreement, without assigning blame. This is the equivalent of a graceful shutdown in a distributed system. Both parties acknowledge that the system is no longer viable. And they agree to terminate the process without a contentious post-mortem. In engineering terms, this is the ideal outcome-it minimizes resource consumption, avoids data loss. And preserves the option for future collaboration (e g. And, co-parenting)

However, the mutual agreement route is often underutilized because it requires both parties to have observability into the same system state. In practice, one partner may perceive the relationship as degraded while the other sees it as stable. This is a classic split-brain problem in distributed consensus: two nodes disagree on the current state of the system, so no agreement is possible. The mutual agreement route only works when both parties have access to the same telemetry-which, in a marriage, requires open communication and shared goals.

For family law platforms, this suggests a need for mediation tools that provide shared dashboards of relationship health. Imagine a mobile app that tracks communication frequency, conflict resolution patterns. And shared financial goals. And presents both partners with a unified view. If the dashboard shows a consistent decline, both parties are more likely to agree on the need for intervention-or, if necessary, a graceful exit. This isn't science fiction; it's a logical extension of existing relationship health apps like Lasting or Couple.

What Software Engineers Can Learn from Singapore's Divorce Data

There are three direct lessons for engineers from this data. First, your classification schema shapes your reality. If your bug tracker only allows "Critical" and "Low" as severity levels, you will miss the Medium-severity bugs that cause the most cumulative damage. Singapore's divorce data is a textbook example of this. Second, high-signal events aren't the most impactful. Adultery is loud but rare; unreasonable behaviour is quiet but pervasive. In production systems, the same is true: silent data corruption (unreasonable behaviour) is far more dangerous than a crash (adultery). Third, graceful shutdowns are a feature, not a bug. The mutual agreement route should be the default, not the exception. In microservices, we design for graceful degradation and shutdown. Why would we not design relationships the same way?

These lessons apply directly to how we build relationship management platforms, mediation tools, and legal tech systems. If you're developing a platform that handles sensitive interpersonal data, you need to account for the complexity of human failure modes. A simple dropdown menu with four options isn't enough. You need multi-label classification, free-text fields. And the ability to update the taxonomy as societal norms evolve. Otherwise, your platform will produce the same misleading data that Singapore's MSF publishes every year.

Technical Recommendations for Legal Tech and Relationship Platforms

Based on this analysis, here are specific engineering recommendations for anyone building in this space:

• Implement multi-label classification: Allow users to select multiple grounds for divorce, with weights or primary/secondary designations. This will produce richer data for analytics and reduce classification bias.
• Build a telemetry layer for relationship health: Develop APIs that ingest data from communication platforms - calendar apps. And financial tools to create a composite score of relationship health. Use differential privacy to protect user anonymity.
• Adopt the SRE error budget model: Define a "relationship error budget" based on agreed-upon thresholds (e g., number of unresolved conflicts per month). When the budget is exhausted, trigger an intervention (counseling, mediation).
• Design for graceful shutdown: Create workflows that guide couples through a mutual separation process, including asset division, custody agreements. And co-parenting plans. This reduces adversarial friction and legal costs.
• Use natural language processing (NLP) for unstructured data: Analyze free-text descriptions of "unreasonable behaviour" to identify common patterns. This can inform policy changes and improve the classification system.

These recommendations aren't theoretical they're based on the same principles that guide site reliability engineering, incident management, and distributed systems design. The only difference is the domain: instead of servers and databases, we're dealing with human emotions and legal frameworks. The engineering mindset is the same.

Frequently Asked Questions

1. Why is unreasonable behaviour the top reason for divorce in Singapore?
   Unreasonable behaviour is the most cited ground because it's a catch-all category that covers a wide range of issues (financial irresponsibility, emotional neglect, incompatibility) and is easier to prove in court than adultery or desertion. The legal system incentivizes petitioners to choose this path because it requires less evidence and is less adversarial.
2. Is adultery really the least common reason for divorce?
   The data says yes. But this is likely an undercount due to classification bias. Many couples cite unreasonable behaviour even when adultery occurred. Because it's simpler and avoids the stigma of proving infidelity. The legal system's taxonomy doesn't capture the full picture.
3. How does this relate to software engineering?
   The divorce data mirrors patterns in distributed systems: unreasonable behaviour is a slow degradation (byzantine failure). While adultery is a catastrophic event (fail-stop). Engineers can learn about the importance of monitoring for gradual degradation, not just crashes, and about the pitfalls of rigid classification schemas.
4. What can be done to improve the data quality?
   Implement multi-label classification for divorce grounds, allow for secondary reasons. And use NLP to analyze unstructured descriptions. This would provide richer data for policymakers and researchers, and reduce the current bias toward unreasonable behaviour as a default category.
5. Are there any tools that apply these principles?
   Yes, relationship health apps like Lasting and Couple track communication patterns,, and but they lack legal integrationPlatforms like OurFamilyWizard focus on co-parenting but don't address divorce classification there's a gap in the market for a platform that combines relationship telemetry with legal workflows.

Conclusion: The Engineering of Human Systems

Singapore's divorce data isn't just a sociological curiosity; it's a case study in how classification systems shape our understanding of complex human phenomena. The fact that "unreasonable behaviour" is the top reason for divorce. While adultery is the least, tells us more about the legal system's taxonomy than about the actual causes of marital breakdown. For engineers, the lesson is clear: design your systems to capture the full complexity of the domain, not just the easy-to-measure events. Whether you're building a bug tracker, a security monitoring platform. Or a family law application, the same principles apply. Use multi-label classification, implement observability, and design for graceful degradation. And remember: the most dangerous failures are often the quietest ones.

If you are building a platform that handles sensitive human data, we can help. At Denver Mobile App Developer, we specialize in designing systems that are both technically robust and ethically sound. Contact us to discuss how we can apply these principles to your next project.

What do you think?

Should legal systems adopt multi-label classification for divorce grounds, or would that create more complexity than it solves?

Is it ethical to apply SRE principles (like error budgets) to personal relationships, or does that cross a line into surveillance?

How can engineers ensure that platforms for sensitive human data (like divorce filings) don't perpetuate the classification biases they are trying to fix?