What might be considered antonyms by most, may well prove to be codependent integral parts of our knowledge accumulation process. On the one hand, consensus assures us of the trust in a theory, “proves” its reliable by synonyms support. On the other, complete agreement should make us weary. Void of criticism, what promise do we have of the legitimacy of a theory? Understandably, consensus and disagreement each serve a different role. Not only does consensus indicates to the validity of a claim, it helps unite researchers under a shared belief to share data. This collaboration could eventually quicken the exploration and aid studies. Contrary, disagreement serves to require further justification for a theory, forces us to constantly validate it. By this logic, both are needed to protect knowledge, the consensus to be the knowledge we hold and the disagreement it’s protector. In order to discuss the topic, we must first define key terms to be used. While considering robust knowledge, two varying agendas come to mind. The first, follows plato’s simple definition – Knowledge is Justified True Belief. To elaborate, by belief we mean the acceptance of a truth or a notion and justification is either empirical evidence for our claim or theoretical reasoning for it. For example, we know the sun will rise tomorrow since this belief is backed up by centuries of sunrises. In contrast, Williams Van Orman Quine proposes a different idea of knowledge. According to his pragmatic criterion of knowledge, it is that which makes our lives better. For instance, he prefers contemporary physics over ancient greek gods as it serves us better. Ergo, contemporary physics is true while the greek gods are not. We will henceforth define Robust Knowledge as that which complies to either of our definitions. Lastly, both consensus and disagreement are intuitively defined as the total agreement in a theory, or lack thereof.Now that we’ve established the key terms needed for the aforementioned claim, we can construct a Knowledge-Question to explore. In this essay, we will try to answer the problem – “What is the role of consensus in judging the quality of knowledge”. In order to do so, we will examine both Mathematics and Natural Sciences as areas of knowledge, and Reason, Perception and Imagination as ways of knowing for them.Firstly, we shall examine the field of mathematics. Here we can clearly see how consensus amongst scholars leads to exponential advancements. When consensus is established, researches can unite together to investigate a matter collaboratively instead of working to disprove it or disagree upon it. The joint work will therefore lead to faster and broader exploration of the subject at hand. This is mainly due to mathematics’ main way of knowing – reason. The joint researches use reason to prove complex theorems coming from each others expertise. For instance, in the field of Linear-Algebra lies a trivial consensus on the understanding and definition of Vectors, ever since its foundation in 1844 by mathematician Hermann Grassmann. This agreement lead to the shared work of many fellow mathematicians such as Arthur Cayley’s and William Rowan Hamilton’s famous Cayley-Hamilton theorem proved in 1853. Furthermore, this knowledge yet proves robust by our first criterion as in mathematics’ knowledge framework proofs serve the role of justification – complying with plato’s definition for knowledge. As a result, consensus plays the role of a catalyst in the accumulation of knowledge.Nevertheless, despite quicking the knowledge acquisition process consensus might prove fatal to robust knowledge. An overwhelming agreement might lead to the blindness in research of opposing areas. While largely based on reason, another fundamental area of knowledge for mathematics is imagination. This stands to the intuinisit understanding of mathematics which claims that it is born out of our mind. According to intuinisists, mathematics is a figment of our imagination, rather than an independent object beyond us as the platonist argue. Ergo, the unity of creative minds in either direction blinds them from the rest. Moreover, according to american philosopher Thomas Kuhn, such paradigm changes are essential to our advancements. For example, since the publication of Euclid’s elements at 300 BC and until the works of János Bolyai and Nikolai Ivanovich Lobachevsky at the 19th century – euclidean geometry stood alone. Euclid’s fifth postulate which stands as the pinnacle of non-euclidean geometries stood unrivaled for all these centuries. Returning to our definition of robust knowledge, we can challenge our knowledge according to our criterion for pragmatic knowledge. According to it, our new knowledge is far more robust as non-euclidean geometry proves essential in current physics while dealing with spaces around object of great mass. From this, we learn that while limited consensus hastens knowledge acquisition, for robust knowledge some disagreement is needed.Moreover, by examining our second Area of Knowledge, Natural Sciences, we find another role for consensus in our knowledge collecting process. At the heart of the Natural sciences methodology are the measurements. In this AoK, we begin by identifying the problem and proposing a hypothesis. Following that, we predict and than test our idea, preceding peer review and replication. Additionally, the importance of experiments is again stressed by the significance of positivism in the Natural Sciences. By claiming that knowledge must be a posteriori, positivists claim that it a theory must be replicated in experiments. Here we identify the consensus’ role at the last stage of our methodology. Consensus following peer-review shows that the peers have agreed and replicated the results of the original experiment. This provides justification for the theory at hand by the the Natural Sciences knowledge framework. By our first criteria, consensus proves that knowledge is robust. An example for this can be seen in the recent theory for quantum-field theory. Emerging in the 1920 it was plagued with oddities and disagreement resulting from countless theories and contradiction. It was not until the establishment of the standard model at 1973, and the november revolution which ended in 1983 that the disagreement passed and consensus was reached. Ever since, we’ve enjoyed a relative agreement, with experiments complying with the standard model verifying its validity. As a result, we attribute the role of validator to consensus. Regardless, one must fear absolute consensus. While consensus might come from universal agreement, it could also come from misinformation. Although originally experiments serve to validate a theory’s authenticity, they could serve the opposite. As science depends on experiments, which are inherently dependent on our perception as a way of knowing – it can be falsified. As our perception proves inaccurate, experiments could as well leading to false theories being accepted. Such inaccuracy could stem from many reasons. Firstly, our eyesight is limited in both its accuracy and range. We can only see light in wavelength between 390 to 700 nm, which leads to many things we simply can’t see. For example, while studying pollination, while birds can see ultraviolet light at 300 nm, we can’t and hence are limited to attribute pollination patterns to visual stimuli by flowers. Furthermore, our optical perception proves inaccurate as we notice from physical visual illusion, such as when mountains seem nearer in clear weather with low humidity, and when straight objects seem bent partially submerged in water.