Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

Yıldızoğlu, Murat

doi:10.1051/ejess:2001105

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…Discrepancies between those outcomes may lead to an update of the mental model. Hence, we have a dynamic structure which evolves as depicted by Figure 2 (Yildizoglu, 2001).…”

Section: Learning With Adaptive Expectationsmentioning

confidence: 99%

“…Other forms of learning processes which, by contrast, do embed feedbacks from experience to the (dynamic) choice of strategies by the individual consumer may however lead to more efficient results. In the following we will analyze three of them, that are usually considered in the learning literature: purely individual adaptive learning based on combination of discovered strategies and random experimenting (Arifovic, 1994;Vriend, 2000;Yildizoglu, 2002;Vallée and Yıldızoğlu, 2009); social learning based on imitation (Arifovic, 1994;Vriend, 2000); adaptive individual learning where the strategies are chosen on the basis of adaptive expectations formed by the agents, as a consequence of their experience in the economy (Yildizoglu, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, we aim to build a framework that is perfectly compatible with bounded rationality, and in which agents can form adaptive expectations by generalizing from their past experience. 4 see Yildizoglu (2001) for an example of this approach in industrial economics.…”

Section: Introductionmentioning

confidence: 99%

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Learning the Optimal Buffer-Stock Consumption Rule of Carroll

et al. 2012

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“…Discrepancies between those outcomes may lead to an update of the mental model. Hence, we have a dynamic structure which evolves as depicted by Figure 2 (Yildizoglu, 2001).…”

Section: Learning With Adaptive Expectationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning the Optimal Buffer-Stock Consumption Rule of Carroll

et al. 2012

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“…In the following we will analyze three of them, that are usually considered in the learning literature: purely individual adaptive learning based on combination of discovered strategies and random experimenting (Arifovic, 1994;Vriend, 2000;Yildizoglu, 2002;Vallée and Yıldızoğlu, 2009); social learning based on imitation (Arifovic, 1994;Vriend, 2000); adaptive individual learning where the strategies are chosen on the basis of adaptive expectations formed by the agents, as a consequence of their experience in the economy (Yildizoglu, 2001).…”

Section: Introductionmentioning

confidence: 99%

Learning the Optimal Buffer-Stock Consumption Rule of Carroll

et al. 2013

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This article questions the rather pessimistic conclusions of Allen and Carroll [Macroeconomic Dynamics5 (2001), 255–271] about the ability of consumers to learn the optimal buffer-stock-based consumption rule. To this end, we develop an agent-based model in which alternative learning schemes can be compared in terms of the consumption behavior that they yield. We show that neither purely adaptive learning nor social learning based on imitation can ensure satisfactory consumption behavior. In contrast, if the agents can form adaptive expectations, based on an evolving individualmental model, their behavior becomes much more interesting in terms of its regularity and its ability to improve performance (which is a clear manifestation of learning). Our results indicate that assumptions on bounded rationality and on adaptive expectations are perfectly compatible with sound and realistic economic behavior, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.

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“…See for example, Fagerberg and Verspagen (2002) for some discussion. 33 Other models employ a more sophisticated and incremental choice between imitation and innovation effort which then does not require common knowledge. See, for instance, Colombo et al (2012).…”

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confidence: 99%

Credit Constrained R&D Spending and Technological Change

Aßmuth

2014

SSRN Journal

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Firms often rely on external financing in order to conduct R&D. The question is to what extent discriminatory behaviour of the funds provider affects the industry evolution. The model is based on an evolutionary framework by Nelson and Winter. A firm chooses its R&D spending in an adaptive fashion where technological improvement is essential for survival in the competitive market. Firms can finance their activities by using retained profits or applying for credit. However, they have a clear hierarchy in choosing the source of funds and saved profits are always used up first. There is endogenous discriminatory lending as the banking sector provides credit according to firms' individual features. It compares profitability and market share across firms when assessing creditworthiness. The model is able to capture features of innovation and diffusion of technology. Results show that the availability of credit is crucial for technological change in a non-linear fashion and that the industry evolves faster if the bank values market share more in assessing creditworthiness.

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Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

Cited by 15 publications

References 10 publications

Learning the Optimal Buffer-Stock Consumption Rule of Carroll

Learning the Optimal Buffer-Stock Consumption Rule of Carroll

Learning the Optimal Buffer-Stock Consumption Rule of Carroll

Credit Constrained R&D Spending and Technological Change

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