Yet, the relationship between scarcity and conflict is far less straightforward than these headlines might suggest. Empirical evidence on the causal link between scarcity and conflict is surprisingly mixed: Sometimes scarcity increases conflict, but in other cases it appears to reduce it. Why does scarcity sometimes ignite tensions, while at other times it seems to encourage restraint and cooperation?

The Spark: Asymmetry

Many high-profile conflicts over scarce open-access resources share a common feature: the actors involved are seldom equal. Consider international negotiations on climate change or fisheries. Disputes in these negotiations rarely arise between countries with comparable histories of resource use. Instead, they typically involve actors with very different past levels of extraction – those who have already taken a large share of the resource and those who are now seeking access. Not surprisingly, tensions emerge when the former try to close the cookie jar just as others are reaching for it.

From the perspective of those who have historically used less of the resource, restrictions may appear deeply unfair. Why should they limit their use when others have already benefited for decades? At the same time, those who have long relied on the resource may feel entitled to their existing share and see it as legitimate or even necessary. When scarcity forces these competing claims into the open, conflict can easily follow.

The Test: A Laboratory Experiment

We set out to test whether asymmetry was the missing link between scarcity and conflict. While contemplating a trip to COP29 in Baku, we remembered how messy these international negotiations can become. We needed a more controlled environment to study the underlying dynamics. An environment that would allow us to causally test whether even a small asymmetry in past resource use could spark conflict among otherwise similar individuals.

We turned to our university laboratory. There, we created a simple game in which student participants jointly managed a renewable resource in the form of a fish pond. In each round, participants could catch fish from the shared resource. They also had the option to destroy part of another participant’s catch, a behavior commonly used in experimental economics as a proxy for conflict and hostility. The more conflict emerged around the use of the shared resource, the more likely participants were to engage in such destructive actions. We then systematically varied two conditions: the scarcity of the resource and the inequality in past resource use.

We induced inequality in past resource use through a simple visual nudge. Participants initially observed only half of the pond, with the other half blackened out. Some saw one half of the pond with plenty of fish, while others saw only a few. The small difference shaped behavior: those who saw more fish tended to take larger initial extractions, while those who saw fewer were more restrained. In this sense, the experimental setup mirrors real-world dynamics. A history of resource-intensive economic growth has led some countries to perceive nature as plentiful and readily exploitable, while others have historically contributed far less to resource exploitation and emissions.

After the first round in the experiment, the veil of ignorance was lifted, and all participants could see the full pond. From that point on, everyone had complete information about the resource, just as we today have common knowledge of the planetary limits. This allowed us to observe how the initial asymmetry influenced subsequent behavior. In many ways, it reflects the current global situation, where the limits of natural resources are increasingly visible to all, even if past patterns of use remain deeply unequal.

The Result: When Scarcity Meets Asymmetry

What we found fit remarkably well with the puzzle that set us on this journey. Scarcity alone did not increase conflict. Instead, it even had a mitigating effect on conflict. Participants engaged in fewer destructive actions against one another when the fish became scarce due to exogenously reduced reproduction. Scarcity seemed to activate a social norm, encouraging greater cooperation in the face of a shared crisis. Faced with the prospect of a shrinking resource, participants appeared less willing to harm others, even as competition over the remaining fish intensified.

Inequality in past resource use alone also did not entail more conflict. Instead, participants tended to coordinate their behavior to reduce the initial imbalance. Those who had taken more initially were more restrained in later rounds, while others increased their extraction. In effect, participants willingly shared the cookie jar to ensure that, by the end of the experiment, everyone received a fair share. This informal coordination helped keep destructive conflict at low levels.

Only when both factors, scarcity and inequality in past resource use, were present simultaneously did the dynamics change. The cooperative social norm appeared to break down, and conflict began to rise. Sustaining such norms of cooperation seemingly becomes more difficult when actors are in markedly different positions due to past resource use.

Figure: Effects of scarcity and asymmetry in past resource use on conflict behavior. Conflict is measured as the destruction of others’ catch. Scarcity and asymmetry individually each reduce destructive behavior, reflecting cooperation and coordination on equitable outcomes. However, when both factors are present simultaneously, conflict increases, indicating a breakdown of cooperative norms.

The Lesson: The Dangers of Asymmetry

Our laboratory experiment carries both hope and caution. On the hopeful side, human behavior appears to include cooperative tendencies that can help societies cope with scarcity. This finding echoes a broader strand of research showing that environmental stress can foster cooperation across many forms of life, from plants and animals to human societies. When facing shared adversity, collective restraint and coordination can emerge even without strong external enforcement.

Yet our findings also highlight how fragile these cooperative tendencies can be. Scarcity itself may not be the greatest danger. Instead, tensions arise when scarcity interacts with unequal histories of resource use. When some actors feel that others have already taken more than their fair share, the willingness to cooperate can quickly erode.

For international negotiations over global commons such as the climate or the oceans, this insight carries an important implication. Managing scarcity alone may not be enough. Policymakers may also need to explicitly address historical asymmetries in resource use. Emphasizing the egalitarian principles of the international system, acknowledging differences in past emissions or catches, and highlighting the shared nature of global commons may help sustain the cooperative norms that scarcity alone can sometimes produce. Framing negotiations in this way may give hope a chance to prevail over the dangers posed by the divide between those who have long had their hands in the cookie jar and those now seeking access.

Reference

This opinion piece is based on the following article:

• Geschwind, S., Lambsdorff, J. G., & Werner, K. (2026). When scarcity intensifies conflict: On the role of asymmetry in common-pool resource games. Ecological Economics, 244, 108963.

Stephan Geschwind Stephan Geschwind is a doctoral researcher at the Chair of Economic Theory at the University of Passau. He holds a Master’s degree in International Economics and Business from the University of Passau and a Bachelor’s degree in International Relations from TU Dresden. His research focuses on behavioral economics, with a particular interest in conflict, sustainability, and collective action.

Prof. Dr. Johann Graf Lambsdorff Prof. Dr. Johann Graf Lambsdorff, distinguished professor of economic theory at the University of Passau since 2003, is recognized worldwide for his work in measuring corruption, experimental economic research on conflict and ethical behavior, institutional economics and the application of AI for teaching. A graduate of the Universities of Frankfurt/Main and Göttingen, he developed the Corruption Perceptions Index for Transparency International in 1995, a tool that has become indispensable for global governance and policy-making in the fight against corruption. His innovation extends to the classEx platform, which allows for interactive experiments and integration of AI and is used by nearly 800 institutions worldwide across various disciplines. He is chairman of the German Association for Experimental Economic Research (GfeW). Graf Lambsdorff’s insights, shared through key publications and as a sought-after keynote speaker, continue to influence economic theory and practice worldwide.

Imagine sending part of your wages home to a family in rural Niger. The trip from the city is long. The informal agent charges a fee of anywhere between 1% and 30% of whatever you send. And even when the money arrives, it may not arrive on time – or at all. Now imagine doing this every month, for years, while living on a low wage in the first place. This is not a hypothetical scenario. It is the everyday reality for millions of migrant workers across Sub-Saharan Africa, caught in what researchers have called a “double whammy”: poverty drives migration, and punishingly high transfer costs erode the wages earned abroad before they ever reach the people who need them most.

Mobile money, such as M-Pesa in Kenya, promises to change this. The evidence that it can do so is compelling: studies from Kenya, Mozambique, Tanzania and beyond show that mobile money can dramatically reduce transaction costs, smooth consumption in times of crisis, and meaningfully reduce poverty. Yet despite mobile money services existing in Niger since 2009, only around 6% of adults had a mobile money account as recently as 2025. Why is a technology with such clear benefits so underused? And what would it actually take to get people to try it?

A randomized controlled trial (RCT) conducted across rural villages in Niger set out to answer these questions – and the results are striking. A modest two-sided financial incentive worth just about USD 4.50 in total led to a more than 200% increase in mobile money account ownership, with effects that persisted even nine months after the intervention ended.

The Problem: Knowing Is Not Enough

It is tempting to assume that low adoption of mobile money reflects a lack of information. If people just knew how it worked, the thinking goes, they would use it. But the Niger study challenges this view directly. The experiment tested two distinct interventions in a village-level randomized design across 978 households (337 in a control group, 641 in treatment groups).

The first was a pure information treatment: enumerators visited households, explained how mobile money worked, and provided a printed flyer. The second added a two-sided financial incentive on top of the information: households received 2,000 CFA francs (roughly USD 3) if they received a mobile money transfer within a set window, and the sender received 1,000 CFA francs (roughly USD 1.50), making the total incentive around USD 4.50.

The information-only treatment increased knowledge about mobile money. But it had no measurable effect on actual adoption. People learned about the technology, but did nothing differently. This is a familiar finding in behavioral science: information alone rarely drives behavior change, particularly when adoption involves unfamiliar technology, upfront effort, and uncertain payoffs. Knowing that something is beneficial is not the same as experiencing it.

The Intervention: Making the First Step Cheap and Social

The incentive treatment worked through two reinforcing mechanisms. First, it made trying mobile money cheap and salient. The small cash reward effectively subsidized the first transaction, making “learning by doing” essentially free. People did not need to trust a system they had never used; they were being paid to find out. This is a classic application of behavioral insights: reducing the cost of a first action to overcome inertia and status-quo bias.

Second, the two-sided design, i.e. rewarding both sender and receiver, deliberately targeted network effects. Mobile money is only valuable if the people you want to send or receive money from are also using it. By incentivizing both sides of a transaction simultaneously, the experiment seeded adoption across social networks, not just within individual households. The result was a self-reinforcing dynamic: as more people in a village started using mobile money, it became easier and more natural for others to follow.

Notably, the information treatment also generated meaningful knowledge spillovers within villages: untreated households in information villages showed increased awareness compared to pure control villages. This suggests that information does travel through social networks, even if it does not, on its own, translate into behavior change.

The Results: A Boost to Adoption and Usage That Lasted

The numbers from the incentive treatment are remarkable. After the intervention, 30% of households in treatment villages held a mobile money account – compared to just 9% in the control group. That is an effect size of over 230%. 20% of households used mobile money and saved via mobile money, an increase of 280% as compared with the control (5%). And crucially, these effects persisted nine months after the intervention had ended, i.e. well beyond the period in which the incentive was active.

On welfare outcomes, the picture is more nuanced. In the short run, no significant consumption-smoothing effects were detected. But over a longer horizon, treated households were more likely to have received a remittance transfer (a 24% increase), and reported higher subjective wellbeing relative to their neighbors. The direction of effects on the number and value of transfers was positive, though not statistically significant, possibly reflecting the short follow-up window relative to the slow-moving dynamics of remittance behavior.

The persistence of adoption is perhaps the most policy-relevant finding. Often, incentivized behavior reverts once the incentive is removed. Here, it did not, suggesting that the intervention succeeded not just in getting people to try mobile money once, but in integrating it into how households manage money.

Effects of information and incentive treatments (relative to control group)
Bars show the share of households having a mobile money account and having used mobile money recently

Notes: *** Effect statistically significant at the 1% level.

Is It Worth It? The Cost-Effectiveness Case

The intervention has a good chance to be cost-effective, both for participants and for policymakers. The total incentive cost per household is modest (roughly USD 4.50), and the downstream benefits – reduced transfer fees, access to formal financial infrastructure, improved consumption-smoothing over time – accumulate. The results imply that the cost of an additional adopter was about 47 USD, this is about 35% less of what comparable interventions have achieved. For mobile network operators and financial inclusion programs, a one-time, low-cost nudge that generates durable adoption should be a compelling investment.

This matters not only for Niger, but for the broader challenge of financial inclusion across Sub-Saharan Africa, where cross-border remittance fees remain among the highest in the world, and where over half the population remains outside the formal financial system despite rapid growth in mobile money infrastructure.

What Policymakers and Practitioners Can Take Away

3. Information is necessary but not sufficient. Awareness campaigns can raise knowledge, but they do not reliably change behavior. Policymakers and financial inclusion programs should not mistake information provision for meaningful intervention.
4. Small, well-designed incentives can have outsized and lasting effects. A subsidy of around USD 4.50 per household, structured to reward both sender and receiver, produced effects that persisted nearly a year after the program ended. The design matters: two-sided incentives that seed network adoption are likely more effective than one-sided individual rewards.
5. Behavioral frictions, not just financial ones, are holding back adoption. The barriers to mobile money use are not primarily about cost or access. They are about the unfamiliarity of a new technology, the absence of a trusted social reference point, and the coordination problem of needing your network to also adopt. Interventions that address these frictions directly, by making first use easy, salient, and social, are more likely to succeed.
6. Low-cost behavioral interventions can be a powerful complement to infrastructure investment. Building the infrastructure for mobile money is necessary. But infrastructure alone is not sufficient if behavioral barriers prevent take-up. Pairing infrastructure rollout with targeted, evidence-based behavioral interventions could substantially accelerate financial inclusion outcomes.

References

This opinion piece is based on the following working paper:

• Aker, J., J. Awonon, M. Grimm, C. Petrik & O. Wirth (2026), If you build it, will they come? Addressing the Barriers to the Adoption of Digital Financial Services. Cornell University.

Further readings:

• Aker, J.C., R. Boumnijel, A. McClelland & N. Tierney (2016). Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger. Economic Development and Cultural Change, 65 (1): 1-3. https://doi.org/10.1086/687578.
• Batista, C. & P.C. Vicente (2025). Is Mobile Money Changing Rural Africa? Evidence from a Field Experiment. Review of Economics and Statistics, 107(3): 835–844. doi: 10.1162/rest_a_01333.
• Klapper, L., D. Singer, L. Starita & A. Norris (2025). The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC. doi: 10.1596/978-1-4648-2204-9.
• Riley, E., A.S. Shonchoy & R. D.Osei (2025). Incentives and Endorsement for Technology Adoption: Evidence from Mobile Banking in Ghana. Journal of Development Economics, 176: 103511. doi: 10.1016/j.jdeveco.2025.103511.
• Suri, T. & W. Jack (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317) : 1288–1292. doi: 10.1126/science.aah5309.

Prof. Dr. Michael Grimm

Michael Grimm is a Professor of Development Economics at the University of Passau. He is also affiliated with  IZA@LISER, the DIW, the RWI Research Network and the Abdul Latif Jameel Poverty Action Lab (J-PAL). He studied in Frankfurt and Paris and earned a PhD at Sciences Po Paris. Before joining the University of Passau, he had positions at the University of Göttingen and the Erasmus University Rotterdam. He was a guest professor at the Paris School of Economics and the University of Bordeaux.  He has been a consultant for the World Bank, for various UN agencies, for Dutch, French, and German donor agencies, and for PARIS21 at the OECD.

From exploring ancient ruins to enjoying Mediterranean cuisine, it was the perfect mix of discovery and connection. We came back inspired, recharged, and even more connected as a team. Already looking forward to the next one!

Missing on the pictures are our dear colleagues Ihsan Alhalawani, Malak Alsaif, Maha Baz, PhD, and Fatima Sala, and Juliane Wiese, PhD.

In boardrooms across the region and beyond, leaders are told that algorithms can understand their customers better than traditional research ever could. The promise is simple: more data equals more insight by AI!

Yet, that equation does not hold. Data reveals what people do; understanding, on the other hand, reveals why they do it. The difference is not academic – it is strategic. It determines whether organizations make informed decisions or misinterpret the very markets they aim to serve. AI can illuminate patterns at unprecedented scale, but meaning still sits on the human side of the equation.

1. The promise – and illusion – of AI-driven insight

Across the Arab region, businesses and governments are adopting AI at remarkable speed. Predictive engines recommend products before consumers ask. Sentiment tools scan live social feeds to flag emerging trends. Real-time analytics, smart dashboards, and automated segmentation have created the sense of a market that is more knowable than ever.

These capabilities are unquestionably powerful:
AI can tailor messaging to individual behavior.
AI can forecast demand by reading subtle shifts in attention.
AI can deliver personalized support at scale.

AI can adjust pricing dynamically.

But these systems excel at correlation, not interpretation or causation. In fact, they map behavior without understanding the cultural, emotional, or social forces that shape it. For example, a surge in online engagement might signal enthusiasm. However, it could just as easily reflect sarcasm, social pressure, disappointment, or coordinated advocacy. Treating patterns as motives collapses the distinction between signal and meaning, creating the illusion of deep insight where none exists.

In fact, true understanding requires context, and context is precisely where AI struggles most…

2. When AI ‘misreads’ the human world: some documented cases

When the AI misinterprets content, the problem often isn’t a ‘technical error’ – the model did exactly what it was trained to do. The failure comes from the fact that human meaning is not reducible to statistical patterns. The machine or AI only sees statistical patterns, not human meaning or implications. These limitations are not theoretical. They have been documented across academic research, industry reports, and real-world deployments. Here are concrete instances where AI fell short:

• Sentiment and sarcasm misclassification in Arabic social media: A recent study analyzing tens of thousands of Arabic-language tweets about generative AI found a measurable amount of sarcasm – a communication form rich in implicit context⁽¹⁾. Even state-of-the-art natural language processing models continue to struggle with sarcasm and irony, especially when cultural or dialectal nuance is involved⁽²⁾. As a result, a post that is sarcastic criticism could be interpreted as praise; a serious risk for brands using sentiment analysis to gauge public opinion.
• Bias and discrimination in automated classification systems: Concerns about algorithmic bias are no longer speculative. Research in AI from multiple sources has highlighted a tension: while algorithms can, in theory, be designed to outperform humans in unbiased perception, real-world systems often inherit and amplify structural biases embedded in data, objectives, and deployment contexts⁽³⁾. The problem is not that machines or AI are inherently biased, but that bias becomes encoded, scaled, and difficult to contest once automated. Moreover, marketing and targeting algorithms can sometimes over-represent some consumer groups and other times render others invisible⁽⁴⁾. These distortions directly affect segmentation, demand estimation, and strategic decision-making which could be questionable to developing ethical and equitable AI systems.​
• Over-personalization and the erosion of trust: Industry and marketing analyses have documented multiple cases where AI-driven personalization crossed the line into intrusion. One widely cited example, discussed in marketing and tech commentary, involved retail recommendation systems inferring sensitive life events (such as pregnancy) before consumers had disclosed them in their families⁽⁵⁾. Rather than signaling relevance, these predictions triggered discomfort and backlash. The failure was not technical accuracy, but a lack of consent and social judgment: prediction without permission.
• Over-reliance on AI in high-stakes decision systems: domains leading to failure: Outside marketing, the promise of AI has sometimes led to disastrous overconfidence. As a high-profile example, an AI-based medical-recommendation system, once heralded as a game-changer, produced unsafe and inaccurate treatment recommendations ⁽⁶⁾. This demonstrates that AI’s “insight” may appear authoritative but can be dangerously misguided when applied without human oversight and contextual judgment.

Taken together, these cases point to a consistent conclusion: AI’s strengths – scale, speed, pattern recognition – do not compensate for its fundamental inability to interpret human meaning and nuance, context, culture, or ethics. Without interpretation, automation merely accelerates misinterpretation.

3. A balanced path: pairing machine scale with human intelligence

Success will come not from replacing humans with machines, but from combining their strengths:

• Hybrid human-AI models: Use AI to flag emerging patterns or weak signals, but keep humans responsible for interpretation, strategy, and final decisions. AI can surface “what changed”, humans decide what it means.
• Reskilling and repositioning human talent: As AI automates data collection and segmentation, human roles should evolve toward strategic interpretation, scenario planning, cultural reading, and ethical judgment; areas where machines remain blind.
• Explainability, transparency, and consent: Systems should be designed so that consumers and researchers understand how data is used, what is inferred, and why. Opt-outs, clear data-use policies, or “human-in-loop” checks should limit misuse or overreach.
• Ethical governance and data protection: Organizations should adopt frameworks to monitor bias, privacy, discrimination, and fairness – not as afterthoughts but as integral parts of insight systems. Regulation analogous to global standards (e.g. GDPR) becomes essential, especially in culturally diverse contexts.

4. In conclusion: Meaning remains human

AI will continue to transform how we gather data and detect signals, but unless we acknowledge its limitations, we risk building insights on shaky foundations. It is true that machines can cluster behavior, forecast probabilities, and highlight patterns. However, they cannot interpret intention, emotion, cultural meaning, or ethical weight. Those remain human domains.

For marketers, researchers, and policymakers, the challenge is clear: Build insight infrastructures where AI accelerates discovery and humans provide anchoring interpretation. Data may fuel the engine, but meaning must come from human thinking, beliefs, and judgement.

AI may shape the future of consumer understanding, but human reasoning remains what guides it responsibly.

References

1. Al-Khalifa, S., Alhumaidhi, F., Alotaibi, H., & Al-Khalifa, H. S. (2023). ChatGPT across Arabic Twitter: A study of topics, sentiments, and sarcasm. Data, 8(11), 171. https://doi.org/10.3390/data8110171
2. Bhargava, N., Radaideh, M. I., Kwon, O. H., Verma, A., & Radaideh, M. I. (2025, April 8). On the impact of language nuances on sentiment analysis with large language models: Paraphrasing, sarcasm, and emojis. arXiv. https://arxiv.org/abs/2504.05603
3. Van Eyghen, Hans (2025). “AI Algorithms as (Un)virtuous Knowers”. Discover Artificial Intelligence. 5(2) 2. doi:1007/s44163-024-00219-z
4. Marabelli, Marco (2024). AI, Ethics, and Discrimination in Business. Palgrave Studies in Equity, Diversity, Inclusion, and Indigenization in Business. Springer. doi:1007/978-3-031-53919-0
5. (2025). Worst ad campaigns: AI’s marketing failures. reelmind Blog. https://reelmind.ai/blog/worst-ad-campaigns-ai-s-marketing-failures
6. Özerdem, H. (2025, October 7). Lessons to be learned from 30 AI fiascos. Medium. https://hakanozerdem.medium.com/lessons-to-be-learned-from-30-ai-fiascos-acc2a61a03ab

Dr. Maha Baz

Dr. Maha Baz is a Senior Researcher / Senior Consultant at Behavia and a Strategic Communication Advisor for Excellence. She has 20 years of experience in strategic marketing, communication, and consumer behavior. She holds a PhD in Marketing from the University of Leicester on the subject of ‘Sharing Economy in the Arab world’ and has written a book chapter about it in the “Handbook on the Sharing Economy”. She advises and consults public and private sector clients on marketing with behavioral insights.

In the evenings, we enjoyed several wonderful dinners, giving us the opportunity to explore the city and connect with our colleagues and dear partners in Saudi. These informal moments added a valuable human dimension to our collaboration and strengthened the team spirit.

A particular highlight of the social program was attending the Riyadh Derby – Al Hilal vs. Al Nassr – live in the stadium. What an incredible experience and a great way to dive into Saudi football culture!