As part of herd analysis, psychological analyses can explain the effects of personality traits, moods and emotions on herd behaviour. From the perspective of cognitive psychology, the Bayesian theories described above focus on the cognitive processes of social learning as a means of acquiring information and knowledge, but the underlying reliance on a hypothesis of rationality limits these analyses. This question is addressed in part in the extensive literature on cognitive biases: cognitive biases can lead to herds because individuals can track a group`s rash decisions for many reasons, including cognitive impairment, environmental cues, and/or framing effects (Tversky and Kahneman 1974; Baddeley et al., 2005). Learning topics also appear in the macroeconomic literature. Topol (1991) analyzed livestock as the result of rational transactions, where traders evaluate information about the prices paid by other traders in relation to their own private judgments about fundamental value. Acemoglu (1992) analyzed the rational learning of other people`s decisions through the extraction of signals from aggregated data. Ideas for imitative learning are also developed in analyses of Austrian economics that examine Hayek`s conclusions on knowledge as a path-dependent process (Hayek 1952; Rizzello, 2004). Along with the sequential theories of the herd of Bayesian theorists, social learning in Austrian economics differs from ordinary problem-solving in that serial processing of information is important, generates dependence on the path, and advances the acquisition of knowledge on a path determined by previous beliefs. Herds and social influence can also reflect the effects of « social emotions »: social norms act as external sanctions that induce negative emotional states when individuals are mavericks (Elster 1998). For imitative behaviors in a broader socio-economic context, social norms will regulate and maintain certain emotions, for example by promoting conformity with certain social and economic norms. Emotional factors can trigger the herd in financial markets when trading with a group moderates a fear reaction, but has the unintended consequence of creating speculative bubbles.

Prechter & Parker (2007) argued that sociological factors will have an impact on macroeconomics, presenting a « socionomic » analysis that emphasizes the importance of social context for decision-making. Reactions in uncertain social situations are different from reactions in isolated situations and/or when the results are safer. In particular, the uncertainty of financial markets creates unconscious and non-rational herds as an instinctive response to endogenous fluctuations. Markets will fluctuate irregularly, reflecting social sentiment, leading to financial instability. This is possible because herd phenomena are consistent with a number of different statistical assumptions – for example, Kirman (1993), using a Markov chain approach, presented his « ants » model, in which ants « convert » by copying another ant; For example, ants faced with two symmetrical food sources tend to focus on one or the other source (rather than evenly distributing between the two). This pattern of behaviour can be interpreted as an ant recruitment activity – if there are positive externalities of foraging behaviour, joint exploitation of one source will bring more benefits to the group than an equal distribution of effort between two different sources (Kirman 1993). Similar models of the herd can be based on Bayesian assumptions: the actions of others represent information used to adjust probabilities and expectations. By updating their probabilities, individuals will apply the Bayesian rule and systematically revise their probabilistic judgments using information about the actions of others, which will create herds and « cascades of information » (Scharfstein and Stein 1990; Banerjee, 1992; Bikhchandani et al., 1992; Avery and Zemsky 1998; Chamley, 2004).

In these Bayesian update models, agents use sophisticated logic in the context of sequential decision-making, but unlike rational expectation models, the outcome can be good or bad, depending on whether the actions of their predecessors send decision-makers down the right or wrong path. Empirically, the herd as a Bayesian learning process agrees with the evidence from a large number of economic experiments (e.g., Anderson and Holt 1996, 1997 and many others), but this evidence does not prove that a Bayesian explanation is superior to other explanations, including those based on ideas from other social sciences. Nevertheless, even in Bayesian models, the basic premise is that economic decisions are essentially the result of a cognitive process in which a mathematical algorithm is used to process information and form expectations. In addition, the emphasis tends to be on a dichotomous division of behaviour into rational or irrational; A person is considered rational if his behavior conforms to the Bayesian update. Sociological influences are limited to learning from the actions of others, and psychological and emotional factors play little role. This article stated that an eclectic approach is essential to understanding how and why herds and social influence develop in an economic and financial context. Ideas and ideas about social influence, imitation and herds were studied using an interdisciplinary approach that brings together a range of ideas from the social and behavioural sciences such as economics, sociology, psychology, evolutionary biology and neuroscience. The most powerful explanations of herds and social influence emphasize the dual role played by reason and emotion. Herds and imitations in economic and financial decision-making may reflect a social learning process, but this is moderated by emotions and socio-psychological traits that determine susceptibility to social influence. This article also confronts the narrow and crude notions of rationality observed in modern economic models of herds and social influence.

The economist`s emphasis on a dichotomous and binary concept of rationality has led to the neglect of important socio-psychological factors; and the focus is only on the immediate mechanisms that lead to rearing (p. e.g., learning, profit, reputation building) has led to neglect of how and why the underlying herd and identity theft trends have evolved to serve more primitive social goals. For future research that combines knowledge from economics and other social sciences with ideas and experimental evidence from neuroscience, neuroeconomics has considerable power to improve our understanding of how reason and emotion interact to create herds in economics and finance. Emotions will play a role in the formation of cognitive biases: by describing the effects of fear and greed on financial decision-making. Shefrin (2002) argued that executive dependence, that is, when decisions are influenced by the context in which they are made, reflects an interaction of cognitive and emotional factors. Emotion and cognition also interact in response to aversion to ambiguity, which Shefrin describes as a fear of the unknown. Emotions influence the use of the « availability heuristic ». This heuristic involves the use of the most easily accessible information, that is to say the easiest to remember: emotions influence memories and thus determine what is remembered and what is forgotten. Some of these emotional factors are beginning to gain prominence in recent economic analyses.

Akerlof & Shiller (2009), for example, developed Keynes` ideas about « animal spirits. » Originally, Keynes analyzed animal spirits only in the context of entrepreneurship, claiming that uncertainty about the future prevents entrepreneurs from correctly calculating the future benefits of their business decisions.