A Reflection on Trust in DeFi


“There would be no need to trust anyone whose activities were continually visible and whose thought processes were transparent, or to trust any system whose workings were wholly known and understood” (Giddens, 1990).

In this series of articles, we shed light on the concept of trust from an engineering and social science perspective. We ping pong the question of what it means to develop trust in a so-called ‘trustless’ space, how we can engineer for trust, and how we can build systems and protocols with strong trust warrants. This series of articles on ‘Trust in DeFi’ consists of five parts:

  1. A Reflection on Trust in DeFi (Josey)
  2. In Code We Trust, or Do We? (Skly)
  3. A Secure Base: Building Confidence Instead of Trust (Josey)
  4. How to Engineer for Trust (Skly)
  5. Identity Fusion: Establishing Long-Lasting Ties Between Protocols/Networks and Users (Josey)

The Evolution of Trust

Human beings are a social species that relies on cooperation to thrive and prosper. In primitive times, trust was essential for survival. If you couldn’t trust your fellow cavemen, you were far less likely to survive. Trusting others was key to building solid relationships and forming cooperation to find food, shelter, and protection. Fast forward to the modern day: We no longer need to rely on others for survival like our ancestors did — at least not under ‘normal circumstances.’ And yet, despite these developments, trust seems just as vital now as it was during the time of our ancestors.

The ability to trust evolved in humans and became hardwired into our brains. Despite its value, the natural ability to trust makes us vulnerable at the same time. Interestingly, and exactly because humans are hard-wired to trust, we find it incredibly difficult to spot deception. We want to believe that most people are trustworthy because it makes us feel safe and secure. However, our natural inclination to trust may be misused by others.

With trust building the foundation of social relationships, it is no surprise that solutions to solve our ‘social problems’ and weaknesses — such as the reliance on trust — are highly sought after. Blockchain technology is seen as such a solution. In other words, at the core of blockchain technology stands the belief that tech could be an ‘impartial’ solution to minimize the uncertainties and risks involved with the need for trust, and thus, social relationships. Blockchain’s ‘trust in code’ ethos aims to substitute for the (socially and politically constituted) trustworthiness of persons, institutions, and governments. Ironically, however, crypto has thrived because of the social network that sustains it — a social network that is built on the very trust that blockchain was meant to replace! A paradox?

The concept of trust is multifaceted and complex. Even more so when it comes to DeFi. Despite its central role in DeFi, trust is vaguely defined and yet needs to be fully explored.

So, let’s dive in.

Let’s start with the trust paradox …

The “Trust Paradox” in Blockchain

“Don’t trust, verify”; the ‘trust concept’ is the centerpiece of blockchain and DeFi. The ‘trust in code’ ethos is rooted in the idea of replacing the social trust model with code, or, in other words: relying on numbers instead of people. The necessity for trust was not just something to be secured by technical means but eliminated altogether (Nakamoto, 2009).

With regards to crypto, this ethos primarily pertains to the notion of impenetrability, and code as incorruptible and impartial as opposed to human institutions and the individuals that manage them (Vidan & Lehdonvirta, 2019). Guided by the belief that automated processes of decision-making are less fallible than human subjectivity, code is considered to be impermeable to greed and fear (Woodall & Ringel, 2019). In theory, consequently, the code serves as a governing mechanism and regulator of trust and social relationships between actors in the network.

However, crypto and its underlying blockchains are more than their features alone. As a peer-production technology, crypto is not just a mere object of necessary logic. It is co-shaped by the social practices around it, and at the same time, shapes social relations through interaction. Crypto is a socio-technological assemblage that consists not only of code but also of a variety of human actors (Hayes, 2019). Having trust in the system ultimately means trusting the entire assemblage of human actors that operate within that network (Lustig & Nardi, 2015).

Thus, what matters just as much as the technology itself is the social environment, i.e., the network of people in which the technology is embedded.

Towards an Understanding of Trust

Trust — A Complex Social Phenomenon

Trust is an inherently multifaceted and complex social phenomenon. It has different, equally legitimate, meanings and refers to a “confident relationship with the unknown” (Botsman, 2020). A possible definition of trust is: “the subjective probability with which one agent assesses that another agent […] will perform a particular action […] independently of his capacity to be able to monitor it, and in a context in which it affects his own action” (Gambetta, 1998). Accordingly, trust inevitably comes with a certain degree of risk and uncertainty, or unpredictability.

Trust in Whom or What? And With What End Goal?

Importantly, trust–risk relations are always only meaningful within a specific ‘transactional context’. This means, there is always a specific object/task with respect to which the trust–risk dynamics operate. Moreover, trust–risk dynamics are often examined in dyadic (trustor–trustee) terms: one party, whether an individual or a group, decides whether/how much to render themselves vulnerable to the actions of another party, be it an individual or group. Thus, simply asking “Do you trust person X?” is meaningless. Trust has several dimensions: Who trusts, who is trusted, and what is the (desired) end goal!? A better, more specific question would be: “Do you trust person X to do A?” Depending on the task and situation, the answer to this question can vary considerably.

One attribute of trust is its interactive, dynamic nature. Trust cannot be simply designed by default; trust must be earned. As an interpersonal, social experience, trust is being negotiated and shaped through the actors in DeFi, with continuous conflicts on what or whom to trust in the rapidly changing ecosystem. Trust is socially generated over time, contextualized to how various stakeholders’ roles are created, legitimized, and negotiated (Strauss, 1978) in the wake of disruptive technology. The chit-chat/general communication channel in Discord, for example, exemplifies how trust-building is deeply embedded in the social fabric of human social interaction.

Importantly, the need for trust becomes more salient when perceived risk is high, particularly in novel domains (Nickel & Vaesen, 2012), such as DeFi. From an innovation perspective, the gap between users’ capabilities and the technological possibilities in DeFi is still enormous. Regardless of that, in their attempt to participate in a new technological revolution, people are trying to educate themselves by consulting others and technologies that they better understand and trust. For example, users typically follow popular figureheads and educate themselves via the most hyped YouTube videos.

So, what does this all mean?

Misleading Trust Assumptions in DeFi

Ability and accessibility play a key role in the ‘trust in code’ assumption in DeFi. The Bitcoin source code can be audited by anyone and from anywhere in the world. Thus, in a practical day-to-day sense, trust is associated with transparency and knowledge/education. Following this premise, the underlying assumption implies: By enhancing transparency, one enhances trust.

Yet, this view is misleading. In fact, quite the opposite is the case. When we design for transparency, we give up on trust (Botsman, 2017) and replace it with control. While control is not the same as trust, these two terms are often confused with each other. We typically take control, when we don’t trust. And, if we were in full control of something, there would be no need for trust. Trust assumptions in DeFi are misleading because they are grounded on a negative argument around the elimination of trust between parties through code. This is a utopian ambition. We cannot design trust into a system. We can only design for trust.

Trust starts where control — or verification through code — ends. Or put differently: As long as humans interact with crypto, the need for trust will not cease to exist. Moreover, as a highly complex and interconnected space, identifying whom and/or what to trust can be challenging. It requires education, time, and effort. Precisely because crypto is highly technical and complex, some degree of trust will always be required.

Nevertheless, apart from the aforementioned trust paradox, blockchain technology can contribute to establishing trust. So, how can we engineer for trust? And how can we build systems/protocols with strong trust warrants?

Our talented programmer and writer, 0xSkly, will grace the DeFi world with answers to these questions in his next article. As much as we appreciate his programming skills, we love his thoughtfully written and vibrantly crafted articles! So, stay tuned…

But for now, bear with me, and let’s have a final, brief look at how blockchain’s deterministic computation can contribute to governing trust.

Deterministic Computation of Blockchain Tech

One way to understand how to disentangle what it means to trust is through the mode of complementary opposites — for a definition of trust and complementary opposites, see Kernel.

Complementary opposites serve as a mode of thought to identify patterns of meaning. Guided by the school of thought of complementary opposites, a better understanding of trust can be achieved by clearly defining what it means to lie and understanding the ways in which it is possible to cheat. Since we can codify the ways in which it is possible to cheat, we can also write executable software rules, with deterministic results, that prevent cheating. Following this understanding, it is commonly assumed that trust is only meaningful if we fully understand how people can lie.

Even though useful, this mode of thought limits trust to the perspective of deterministic computation. Accordingly, to understand trust, one must know the details of all possible deceptions. This perspective ties back to the aforementioned argument regarding control. If we fully understood how people can lie, and encode for all eventualities that people are going to lie, we wouldn’t need to trust anymore. Moreover, defining all ways that it’s possible to cheat is nearly impossible. Crypto is much more than deterministic ways of interaction and normative code. Equally, trust is more subtle and fine-grained than deterministic values.


To sum up: blockchain technology is guided by the ethos that it is an object of trust by design, and therefore a mediator of trust by default. The clearly defined and encoded rules, however, do not mean we no longer need to trust. On the contrary! It means that there is an implicit shift from trusting crypto’s materiality as a ‘trust-free’ technology to trusting participants within the network with whom we are actually transacting and interacting. This comes with an additional challenge related to the nature of crypto: Establishing trust in a pseudonymous, decentralized space involves trust mechanisms that haven’t been explored yet!

As an infinite design space, DeFi holds enormous potential for innovation. But there are layers and layers of unfolding complexity that still need to be made sense of.

A deep dive into the socio-technological layers of DeFi reveals the sources of some of DeFi’s inherent complexity (see ‘sense-making’ study). As a socio-technological system, DeFi builds on and unites concepts of inherently conflicting worldviews and underlying assumptions, which require different mechanisms to make a system work. The different perceptions and meanings of trust are a reflection of just that — a contrasting belief set that, when united, grows to its fullest potential, building a coherent and powerful whole. Finally, the socio-technological gap of trust is the divide between what we know we must support socially and what we can support technically.

More about that in the next article. Stay tuned!


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Gambetta, D. (1998). Can We Trust Trust? In D. Gambetta (Ed.), Trust: Making and Breaking Cooperative Relations. Blackwell, Oxford, 1998, pp. 213–238.

Giddens, A. (1990). The Consequences of Modernity. Polity Press, Cambridge.

Hayes, A. (2019). The Socio-Technological Lives of Bitcoin. Theory, Culture & Society, 36(4), 49–72. https://doi.org/10.1177/0263276419826218

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Lustig, C. & Nardi, B. (2015). Algorithmic Authority: The Case of Bitcoin. In Misra S., et al. (eds.) 2015 48th Hawaii International Conference on System Sciences, IEEE, 743–752.

Mallard, A., Méadel, C. & Musiani, F. (2014). The Paradoxes of Distributed Trust: Peer-to-Peer Architecture and User Confidence in Bitcoin. Journal of Peer Production, 7(10), 1–10. https://hal-mines-paristech.archives-ouvertes.fr/hal-00985707.

Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf

Nickel, P. J. (2015). Design for the Value of Trust . In van den Hoven, J., Vermaas, P., and van de Poel, I. (eds.), Handbook of Ethics, Values, and Technological Design. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6970-0_21

Nickel, P. J. & Vaesen, K. (2012). Risk and Trust. In Hillerbrand, R., Sandin, P., Peterson, M. (eds.), Handbook of Risk Theory. Springer, Dordrecht. DOI: 10.1007/978–94–007–1433–5.

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Vidan, G. & Lehdonvirta, V. (2019). Mine the Gap: Bitcoin and the Maintenance of Trustlessness. New Media & Society, 21(1), 42–59. https://doi.org/10.1177/1461444818786220

Woodall, A. & Ringel, S. (2019). Blockchain Archival Discourse: Trust and the Imaginaries of Digital Preservation. New Media & Society, 22(12), 2200–2217. https://doi.org/10.1177/1461444819888756

A Reflection on Trust in DeFi was originally published in Balancer Protocol on Medium, where people are continuing the conversation by highlighting and responding to this story.