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BİYOLOJİK KISITLARDAN SENTETİK RASYONELLİĞE: KARAR ALMA SÜRECİNİN EVRİMSEL PARADİGMA ANALİZİ VE "ROBO-ECONOMICUS"UN DOĞUŞU

Yıl 2026, Cilt: 50 Sayı: 1, 81 - 94, 25.03.2026

Ömür Saltık , Süleyman Değirmen

https://izlik.org/JA53ZS53UK

Öz

Finansal iktisadın geçirdiği epistemolojik evrim, özünde Mill (1844) ile von Neumann ve Morgenstern (1944) tarafından kurgulanan, duygulardan arındırılmış, tutarlı ve sınırsız işlem gücüne sahip aksiyomatik “Homo Economicus” idealine dayanmaktadır. Ancak bu katı matematiksel çerçeve, Simon’ın (1955) “Sınırlı Rasyonellik” yaklaşımı ve Kahneman ile Tversky’nin (1979) davranışsal bulgularıyla önemli bir kırılma yaşamıştır. Güncel nöroiktisat ve nörofinans literatürü, rasyonellikten sapmaların basit psikolojik tercihlerden değil, insan beyninin işlem hızı, enerji tasarrufu ve bilişsel kapasite gibi doğrudan biyolojik kısıtlarından kaynaklandığını ortaya koymaktadır. Bu çalışma, finansal rasyonellik kavramının tarihsel dönüşümünü geleneksel, davranışsal, nörolojik ve sentetik rasyonellik olmak üzere dört temel paradigma çerçevesinde ele almaktadır. Temel amaç, insan zihninin doğuştan gelen biyolojik sınırlarının modern yapay zeka mimarileri aracılığıyla nasıl ikame edildiğini analiz etmektir. Tartışmanın kuramsal zemini, erken dönem mantıksal nöron modellerinden Transformer mimarilerine ve zincirleme düşünce yeteneklerine uzanan teknolojik gelişmelere dayanmaktadır. Bulgular, bu ilerlemelerin unutma, dikkat darboğazı ve bilişsel uyumsuzluk gibi insani kısıtları aşarak, iktisadi karar vericinin Homo Sapiens’ten hafıza ve muhakeme kapasitesi yüksek bir Robo-Economicus tipolojisine evrildiğini göstermektedir

Anahtar Kelimeler

Finansal İktisat Davranışsal İktisat Yapay Zeka Sentetik Rasyonellik Robo-Economicus

Etik Beyan

Gerçekleştirilen araştırmanın klinik izinleri, Mersin Üniversitesi Klinik Araştırma Etik Kurulu'nun onayı ile verilmiştir. Çalışmanın, 1964 Helsinki Bildirgesi ve sonraki değişikliklerinde belirtilen etik standartlara veya benzer etik standartlara uygun olarak gerçekleştirildiğini onaylarız

Destekleyen Kurum

Mersin Üniversitesi Bilimsel Araştırma Birimi

Proje Numarası

2018-2-TP3-2986

Teşekkür

Bu tez Mersin Üniversitesi BAP Birimi “2018-2-TP3-2986” Doktora Tez Projesi (TP3) kapsamında desteklenmiştir.

Kaynakça

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-515.
  • Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23–36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine J. M., Breton Provencher, V., Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. The Journal of Neuroscience, 42(45), 8514–8523.
  • DeepSeek-AI, Liu, A., Feng, B., Wang, B., Wang, B., Liu, B., Zhao, C., ... & Xie, Z.. (2024a). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434.
  • DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., ... & Pan, Z.. (2024b). DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437.
  • DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dummel, S., Rummel, J., & Voss, A. (2016). Additional information is not ignored: New evidence for information integration and inhibition in take-the-best decisions. Acta psychologica, 163, 167-184.
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Faris, A. A., Jwan, H. K., & Al-Bidairi, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191–204.
  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
  • Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  • Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.
  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.
  • Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146–162.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.
  • Howarth, C., Gleeson, P., & Attwell, D. (2012). Updated energy budgets for neural computation in the neocortex and cerebellum. Journal of Cerebral Blood Flow & Metabolism, 32(7), 1222-1232.
  • Jordan, M. I. (1986). Serial order: A parallel distributed processing approach (ICS Report 8604). La Jolla, CA: University of California, San Diego.
  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
  • Kahneman, D., Wakker, P. P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–405.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.
  • Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.
  • Kahneman, D. (2011). Fast and slow thinking. Allen Lane and Penguin Books, New York.
  • Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin Company.
  • Lebiere, C., & Anderson, J. R. (2011). Cognitive constraints on decision making under uncertainty. Frontiers in psychology, 2, 305.
  • Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615.
  • Markov, A. A. (1913). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. (Trans. D. Link, 2006). Science in Context, 19(4), 591–600.
  • Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151–158.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  • Mill, J. S. (1844). Essays on some unsettled questions of political economy. London: John W. Parker. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
  • Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts's "Logical calculus of ideas immanent in nervous activity". Synthese, 141(2), 175–215.
  • Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1-2), 122–136.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training [Technical Report]. OpenAI. Available at: http://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  • Raeini, M. G. (2025). The evolution of language models: From N-Grams to LLMs, and beyond. Natural Language Processing Journal, 12, 100168.
  • Ramana, S. V., Reddy, Y. S., Ram, P. P., & Rao, S. S. (2024). Neuro economics and financial decision-making: Bridging the gap with behavioral finance. Educational Administration: Theory and Practice, 30(5), 1035–1044.
  • Rashid, M., Ahmad, R., & Tariq, S. (2022). Financial revolution: From traditional finance to behavioral and neuro-finance. South Asian Journal of Social Sciences & Humanities, 3(4), 95–108.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
  • Selden, G. C. (1912). Psychology of the stock market. New York: Ticker Publishing.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
  • Shefrin, H., & Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29(3), 323–349.
  • Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
  • Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 1119-1151.
  • Srivastava, M., Sharma, G. D., & Srivastava, A. K. (2019). Human brain and financial behavior: A neurofinance perspective. International Journal of Ethics and Systems, 35(4), 485–503.
  • Stearns, S. C. (2000). Daniel Bernoulli (1738): evolution and economics under risk. Journal of biosciences, 25(3), 221-228.
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27, 3104–3112.
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
  • Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
  • Tseng, K. C. (2006). Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4), 7–18.
  • Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological bulletin, 76(2), 105.
  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.
  • Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
  • Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
  • Wichary, S., & Smolen, T. (2016). Neural underpinnings of decision strategy selection: a review and a theoretical model. Frontiers in neuroscience, 10, 500.
  • Wichary, S., Magnuski, M., Oleksy, T., & Brzezicka, A. (2017). Neural signatures of rational and heuristic choice strategies: A single trial ERP analysis. Frontiers in human neuroscience, 11, 401.

From Biological Constraints to Synthetic Rationality: An Evolutionary Paradigm Analysis of Decision Making and the Rise of "Robo-Economicus"

Yıl 2026, Cilt: 50 Sayı: 1, 81 - 94, 25.03.2026

Ömür Saltık , Süleyman Değirmen

https://izlik.org/JA53ZS53UK

Öz

The epistemological evolution of financial economics is fundamentally rooted in the axiomatic “Homo Economicus” ideal articulated by Mill (1844) and von Neumann and Morgenstern (1944), which assumes emotion-free, internally consistent decision-making supported by unlimited computational capacity. However, this rigid mathematical framework experienced a major rupture with Simon’s (1955) concept of bounded rationality and the behavioral evidence provided by Kahneman and Tversky (1979). Contemporary neuroeconomics and neurofinance literature demonstrates that deviations from rationality do not merely stem from psychological preferences, but rather arise from inherent biological constraints of the human brain, such as processing speed, energy efficiency, and limited cognitive capacity. This study examines the historical transformation of financial rationality through four core paradigms: traditional, behavioral, neurological, and synthetic rationality. Its primary objective is to analyze how the innate biological limits of human cognition are increasingly substituted by modern artificial intelligence architectures. The theoretical foundation of the discussion spans technological developments ranging from early logical neuron models to Transformer architectures and advanced chain-of-thought reasoning mechanisms. The findings indicate that these advancements effectively overcome human limitations such as forgetting, attentional bottlenecks, and cognitive dissonance, thereby signaling a transition of economic decision-makers from biologically constrained Homo Sapiens toward a Robo-Economicus typology characterized by enhanced memory and reasoning capacity.

Anahtar Kelimeler

Financial Economics Neurofinance Synthetic Rationality Robo-Economicus Chain-of-Thought Transformer Architecture

Proje Numarası

2018-2-TP3-2986

Kaynakça

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-515.
  • Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23–36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine J. M., Breton Provencher, V., Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. The Journal of Neuroscience, 42(45), 8514–8523.
  • DeepSeek-AI, Liu, A., Feng, B., Wang, B., Wang, B., Liu, B., Zhao, C., ... & Xie, Z.. (2024a). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434.
  • DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., ... & Pan, Z.. (2024b). DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437.
  • DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dummel, S., Rummel, J., & Voss, A. (2016). Additional information is not ignored: New evidence for information integration and inhibition in take-the-best decisions. Acta psychologica, 163, 167-184.
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Faris, A. A., Jwan, H. K., & Al-Bidairi, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191–204.
  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
  • Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  • Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.
  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.
  • Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146–162.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.
  • Howarth, C., Gleeson, P., & Attwell, D. (2012). Updated energy budgets for neural computation in the neocortex and cerebellum. Journal of Cerebral Blood Flow & Metabolism, 32(7), 1222-1232.
  • Jordan, M. I. (1986). Serial order: A parallel distributed processing approach (ICS Report 8604). La Jolla, CA: University of California, San Diego.
  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
  • Kahneman, D., Wakker, P. P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–405.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.
  • Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.
  • Kahneman, D. (2011). Fast and slow thinking. Allen Lane and Penguin Books, New York.
  • Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin Company.
  • Lebiere, C., & Anderson, J. R. (2011). Cognitive constraints on decision making under uncertainty. Frontiers in psychology, 2, 305.
  • Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615.
  • Markov, A. A. (1913). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. (Trans. D. Link, 2006). Science in Context, 19(4), 591–600.
  • Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151–158.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  • Mill, J. S. (1844). Essays on some unsettled questions of political economy. London: John W. Parker. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
  • Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts's "Logical calculus of ideas immanent in nervous activity". Synthese, 141(2), 175–215.
  • Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1-2), 122–136.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training [Technical Report]. OpenAI. Available at: http://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  • Raeini, M. G. (2025). The evolution of language models: From N-Grams to LLMs, and beyond. Natural Language Processing Journal, 12, 100168.
  • Ramana, S. V., Reddy, Y. S., Ram, P. P., & Rao, S. S. (2024). Neuro economics and financial decision-making: Bridging the gap with behavioral finance. Educational Administration: Theory and Practice, 30(5), 1035–1044.
  • Rashid, M., Ahmad, R., & Tariq, S. (2022). Financial revolution: From traditional finance to behavioral and neuro-finance. South Asian Journal of Social Sciences & Humanities, 3(4), 95–108.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
  • Selden, G. C. (1912). Psychology of the stock market. New York: Ticker Publishing.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
  • Shefrin, H., & Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29(3), 323–349.
  • Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
  • Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 1119-1151.
  • Srivastava, M., Sharma, G. D., & Srivastava, A. K. (2019). Human brain and financial behavior: A neurofinance perspective. International Journal of Ethics and Systems, 35(4), 485–503.
  • Stearns, S. C. (2000). Daniel Bernoulli (1738): evolution and economics under risk. Journal of biosciences, 25(3), 221-228.
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27, 3104–3112.
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
  • Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
  • Tseng, K. C. (2006). Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4), 7–18.
  • Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological bulletin, 76(2), 105.
  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.
  • Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
  • Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
  • Wichary, S., & Smolen, T. (2016). Neural underpinnings of decision strategy selection: a review and a theoretical model. Frontiers in neuroscience, 10, 500.
  • Wichary, S., Magnuski, M., Oleksy, T., & Brzezicka, A. (2017). Neural signatures of rational and heuristic choice strategies: A single trial ERP analysis. Frontiers in human neuroscience, 11, 401.

Yıl 2026, Cilt: 50 Sayı: 1, 81 - 94, 25.03.2026

Ömür Saltık , Süleyman Değirmen

https://izlik.org/JA53ZS53UK

Öz

Proje Numarası

2018-2-TP3-2986

Kaynakça

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-515.
  • Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23–36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine J. M., Breton Provencher, V., Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. The Journal of Neuroscience, 42(45), 8514–8523.
  • DeepSeek-AI, Liu, A., Feng, B., Wang, B., Wang, B., Liu, B., Zhao, C., ... & Xie, Z.. (2024a). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434.
  • DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., ... & Pan, Z.. (2024b). DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437.
  • DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dummel, S., Rummel, J., & Voss, A. (2016). Additional information is not ignored: New evidence for information integration and inhibition in take-the-best decisions. Acta psychologica, 163, 167-184.
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Faris, A. A., Jwan, H. K., & Al-Bidairi, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191–204.
  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
  • Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  • Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.
  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.
  • Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146–162.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.
  • Howarth, C., Gleeson, P., & Attwell, D. (2012). Updated energy budgets for neural computation in the neocortex and cerebellum. Journal of Cerebral Blood Flow & Metabolism, 32(7), 1222-1232.
  • Jordan, M. I. (1986). Serial order: A parallel distributed processing approach (ICS Report 8604). La Jolla, CA: University of California, San Diego.
  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
  • Kahneman, D., Wakker, P. P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–405.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.
  • Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.
  • Kahneman, D. (2011). Fast and slow thinking. Allen Lane and Penguin Books, New York.
  • Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin Company.
  • Lebiere, C., & Anderson, J. R. (2011). Cognitive constraints on decision making under uncertainty. Frontiers in psychology, 2, 305.
  • Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615.
  • Markov, A. A. (1913). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. (Trans. D. Link, 2006). Science in Context, 19(4), 591–600.
  • Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151–158.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  • Mill, J. S. (1844). Essays on some unsettled questions of political economy. London: John W. Parker. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
  • Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts's "Logical calculus of ideas immanent in nervous activity". Synthese, 141(2), 175–215.
  • Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1-2), 122–136.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training [Technical Report]. OpenAI. Available at: http://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  • Raeini, M. G. (2025). The evolution of language models: From N-Grams to LLMs, and beyond. Natural Language Processing Journal, 12, 100168.
  • Ramana, S. V., Reddy, Y. S., Ram, P. P., & Rao, S. S. (2024). Neuro economics and financial decision-making: Bridging the gap with behavioral finance. Educational Administration: Theory and Practice, 30(5), 1035–1044.
  • Rashid, M., Ahmad, R., & Tariq, S. (2022). Financial revolution: From traditional finance to behavioral and neuro-finance. South Asian Journal of Social Sciences & Humanities, 3(4), 95–108.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
  • Selden, G. C. (1912). Psychology of the stock market. New York: Ticker Publishing.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
  • Shefrin, H., & Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29(3), 323–349.
  • Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
  • Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 1119-1151.
  • Srivastava, M., Sharma, G. D., & Srivastava, A. K. (2019). Human brain and financial behavior: A neurofinance perspective. International Journal of Ethics and Systems, 35(4), 485–503.
  • Stearns, S. C. (2000). Daniel Bernoulli (1738): evolution and economics under risk. Journal of biosciences, 25(3), 221-228.
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27, 3104–3112.
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
  • Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
  • Tseng, K. C. (2006). Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4), 7–18.
  • Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological bulletin, 76(2), 105.
  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.
  • Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
  • Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
  • Wichary, S., & Smolen, T. (2016). Neural underpinnings of decision strategy selection: a review and a theoretical model. Frontiers in neuroscience, 10, 500.
  • Wichary, S., Magnuski, M., Oleksy, T., & Brzezicka, A. (2017). Neural signatures of rational and heuristic choice strategies: A single trial ERP analysis. Frontiers in human neuroscience, 11, 401.

Yıl 2026, Cilt: 50 Sayı: 1, 81 - 94, 25.03.2026

Ömür Saltık , Süleyman Değirmen

https://izlik.org/JA53ZS53UK

Öz

Proje Numarası

2018-2-TP3-2986

Kaynakça

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-515.
  • Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23–36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine J. M., Breton Provencher, V., Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. The Journal of Neuroscience, 42(45), 8514–8523.
  • DeepSeek-AI, Liu, A., Feng, B., Wang, B., Wang, B., Liu, B., Zhao, C., ... & Xie, Z.. (2024a). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434.
  • DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., ... & Pan, Z.. (2024b). DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437.
  • DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dummel, S., Rummel, J., & Voss, A. (2016). Additional information is not ignored: New evidence for information integration and inhibition in take-the-best decisions. Acta psychologica, 163, 167-184.
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Faris, A. A., Jwan, H. K., & Al-Bidairi, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191–204.
  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
  • Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  • Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.
  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.
  • Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146–162.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.
  • Howarth, C., Gleeson, P., & Attwell, D. (2012). Updated energy budgets for neural computation in the neocortex and cerebellum. Journal of Cerebral Blood Flow & Metabolism, 32(7), 1222-1232.
  • Jordan, M. I. (1986). Serial order: A parallel distributed processing approach (ICS Report 8604). La Jolla, CA: University of California, San Diego.
  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
  • Kahneman, D., Wakker, P. P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–405.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.
  • Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.
  • Kahneman, D. (2011). Fast and slow thinking. Allen Lane and Penguin Books, New York.
  • Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin Company.
  • Lebiere, C., & Anderson, J. R. (2011). Cognitive constraints on decision making under uncertainty. Frontiers in psychology, 2, 305.
  • Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615.
  • Markov, A. A. (1913). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. (Trans. D. Link, 2006). Science in Context, 19(4), 591–600.
  • Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151–158.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  • Mill, J. S. (1844). Essays on some unsettled questions of political economy. London: John W. Parker. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
  • Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts's "Logical calculus of ideas immanent in nervous activity". Synthese, 141(2), 175–215.
  • Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1-2), 122–136.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training [Technical Report]. OpenAI. Available at: http://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  • Raeini, M. G. (2025). The evolution of language models: From N-Grams to LLMs, and beyond. Natural Language Processing Journal, 12, 100168.
  • Ramana, S. V., Reddy, Y. S., Ram, P. P., & Rao, S. S. (2024). Neuro economics and financial decision-making: Bridging the gap with behavioral finance. Educational Administration: Theory and Practice, 30(5), 1035–1044.
  • Rashid, M., Ahmad, R., & Tariq, S. (2022). Financial revolution: From traditional finance to behavioral and neuro-finance. South Asian Journal of Social Sciences & Humanities, 3(4), 95–108.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
  • Selden, G. C. (1912). Psychology of the stock market. New York: Ticker Publishing.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
  • Shefrin, H., & Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29(3), 323–349.
  • Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
  • Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 1119-1151.
  • Srivastava, M., Sharma, G. D., & Srivastava, A. K. (2019). Human brain and financial behavior: A neurofinance perspective. International Journal of Ethics and Systems, 35(4), 485–503.
  • Stearns, S. C. (2000). Daniel Bernoulli (1738): evolution and economics under risk. Journal of biosciences, 25(3), 221-228.
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27, 3104–3112.
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
  • Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
  • Tseng, K. C. (2006). Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4), 7–18.
  • Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological bulletin, 76(2), 105.
  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.
  • Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
  • Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
  • Wichary, S., & Smolen, T. (2016). Neural underpinnings of decision strategy selection: a review and a theoretical model. Frontiers in neuroscience, 10, 500.
  • Wichary, S., Magnuski, M., Oleksy, T., & Brzezicka, A. (2017). Neural signatures of rational and heuristic choice strategies: A single trial ERP analysis. Frontiers in human neuroscience, 11, 401.

Yıl 2026, Cilt: 50 Sayı: 1, 81 - 94, 25.03.2026

Ömür Saltık , Süleyman Değirmen

https://izlik.org/JA53ZS53UK

Öz

Proje Numarası

2018-2-TP3-2986

Kaynakça

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
  • Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-515.
  • Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23–36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine J. M., Breton Provencher, V., Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. The Journal of Neuroscience, 42(45), 8514–8523.
  • DeepSeek-AI, Liu, A., Feng, B., Wang, B., Wang, B., Liu, B., Zhao, C., ... & Xie, Z.. (2024a). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434.
  • DeepSeek-AI, Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., ... & Pan, Z.. (2024b). DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437.
  • DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dummel, S., Rummel, J., & Voss, A. (2016). Additional information is not ignored: New evidence for information integration and inhibition in take-the-best decisions. Acta psychologica, 163, 167-184.
  • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Faris, A. A., Jwan, H. K., & Al-Bidairi, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191–204.
  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650.
  • Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: the recognition heuristic. Psychological review, 109(1), 75.
  • Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.
  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62(2011), 451-482.
  • Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146–162.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.
  • Howarth, C., Gleeson, P., & Attwell, D. (2012). Updated energy budgets for neural computation in the neocortex and cerebellum. Journal of Cerebral Blood Flow & Metabolism, 32(7), 1222-1232.
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  • Kahneman, D., Wakker, P. P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics, 112(2), 375–405.
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  • Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts's "Logical calculus of ideas immanent in nervous activity". Synthese, 141(2), 175–215.
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilim ve Teknoloji Sosyolojisi ve Sosyal Bilimler, Karar Verme, Bilişsel ve Hesaplamalı Psikoloji (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Ömür Saltık 0000-0001-8507-8971

Süleyman Değirmen 0000-0001-8750-652X

Proje Numarası 2018-2-TP3-2986
Gönderilme Tarihi 4 Şubat 2026
Kabul Tarihi 6 Mart 2026
Yayımlanma Tarihi 25 Mart 2026
IZ https://izlik.org/JA53ZS53UK
Yayımlandığı Sayı Yıl 2026 Cilt: 50 Sayı: 1

Kaynak Göster

APA Saltık, Ö., & Değirmen, S. (2026). BİYOLOJİK KISITLARDAN SENTETİK RASYONELLİĞE: KARAR ALMA SÜRECİNİN EVRİMSEL PARADİGMA ANALİZİ VE "ROBO-ECONOMICUS"UN DOĞUŞU. Cumhuriyet Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, 50(1), 81-94. https://izlik.org/JA53ZS53UK

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