Social norms are often characterized as a system of rules that guide behavior. However, social norms also allow for flexibility; not entirely restricting individuals to one possible behavior. Here, we put forward an agent-based cognitive model that captures social norms as processes that are socially constructed through interactions between individuals. In this modelling work, we focus on the role of norm acquisition and conformity bias in both action production and inference-making. This computational cognitive model allows us to think about social norms along three dimensions: individual vs. collective, behavior vs. belief, and subjective vs. objective. Our simulation results show that increased conformity bias can induce misjudgments about the true desires of others and misalignment between different agents' perceptions of the social norm. However, if agents do not assume that others also conform in their behavior, this increased conformity bias does not necessarily lead to excessive misperceptions of the social norm.

Large numbers play a significant role in personal and political financial choices and the understanding of exponentialgrowth. Large numbers are also often misjudged, showing a logarithmic number understanding. Small numbers are how-ever represented in a linear fashion, due to direct experience on for example number lines. Earlier, it was shown that largenumber comprehension can be trained, influencing societally relevant choices. We trained large number comprehensionusing a serious game (RunTheLine): an infinite runner game where an avatar runs on a number line ranging till one billion.Due to the game mechanics, the players walk the number line at both small and large numbers in small steps, making themaware of the continuity of the number line. Pre-post test differences show a change in economic judgments compared toa control group. This offers a scientific manipulation of behavioral and cortical number line representations and potentialeducational applications.