# Modelling and Measuring Trust in Human-Robot Collaboration
## 2 Background
### Theoretical Foundations of Trust
Some differentiate between moralistic trust, based on previous beliefs about behaviour, and strategic trust, based on individual experiences [10]. Other perspectives delineate dispositional, situational, and learned trust as distinct categories, where dispositional traits--such as age, race, education, and cultural background--influence an individual's predisposition to trust, situational factors acknowledge conditions influencing trust formation (e.g., variations in risk), and learned trust reflects iterative development shaped by past interactions [11]. Another approach categorizes the nature of trust into three facets: Referents of Trust, which encompass phenomena to which trust pertains, such as individual attitudes and social influences; Components of Trust, which encompass the sentiments inherent in trust, including consequential and motivational aspects; and Dimensions of Trust, outlining the evaluative aspects of trust, including its incremental and symmetrical nature. To avoid ambiguity between the terms \"Trust dimensions\" and \"Dimensions of Trust\", we will use the denominations \"Phenomenon-based\", \"Sentiment-based\", and \"Judgement-based\" [12]. Trust is also described as a combination of individual trust (derived from own personal characteristics and conforming to logical trust and emotional trust) and relational trust, referencing the dimensions of trust that rise from the relationship with other entities [2]. Even if different authors use different classifications, it is possible to map similarities between different approaches. Despite omitting minor particularities, Table 1 shows the convergence of these classifications. For convenience, we will use the nomenclature defined in [11]--dispositional, situational and learned trust--without loss of generality. In essence, trust relies on multiple complex factors, encompassing both individual and relational aspects. Notwithstanding the diverse disciplinary perspectives and methodologies in researching trust, there is considerable consensus on the fundamental concept and dimensions of trust. However, understanding, modelling, and measuring trust, particularly in human-to-machine contexts, continue to pose considerable challenges. Tackling these challenges requires an in-depth understanding of multifaceted trust dynamics. The primary challenge lies in encapsulating the complexities of human trust in a computational model, given its subjectivity and dynamic nature. Quantifying trust is another significant obstacle, as trust is an internal and deeply personal emotion. The novelty of trust in the Human-Robot Collaboration domain implies a lack of historical data and testing methodologies to build the trust models upon. Furthermore, implementing these models in real-world scenarios is another challenge due to constraints related to resources, variability in responses and the need for instantaneous adaptation. Despite these hurdles, the potential rewards of successfully modelling and measuring trust in Human-Robot Collaborations--including enhanced efficiency, increased user acceptance and improved safety--are immense.