Using Symbolic Regression to Infer Strategies from Experimental Data

Duffy, John; Engle‐Warnick, Jim

doi:10.1007/978-3-7908-1784-3_4

Cited by 40 publications

(33 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although symbolic regression is typically used to find explicit (12)(13)(14) and differential equations (15), this method cannot readily find conservation laws or invariant equations. Rather than trying to model a specific signal, we are trying to detect any underlying physical law that the system obeys, which may or may not be constant (e.g., a Lagrangian).…”

Section: Distilling Free-form Natural Laws From Experimental Datamentioning

confidence: 99%

Distilling Free-Form Natural Laws from Experimental Data

Schmidt

Lipson

2009

Science

2,225

1,637

View full text Add to dashboard Cite

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.

show abstract

Section: Distilling Free-form Natural Laws From Experimental Datamentioning

confidence: 99%

Distilling Free-Form Natural Laws from Experimental Data

Schmidt

Lipson

2009

Science

2,225

1,637

View full text Add to dashboard Cite

show abstract

“…Obviously, the notion of rationality applied here is ecological [15], [16]. 5 [17] provides the first illustration of using genetic programming to infer the behavioral rules of human agents in the context of ultimatum game experiments.…”

Section: A Genetic Programming As a Rule-inference Enginementioning

confidence: 99%

Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets

Kampouridis

Chen

Tsang

2011

2011 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)

View full text Add to dashboard Cite

Abstract-This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments in that work took place under a single financial market and investigated whether its behavior is non-stationary or cyclic. Results showed that the market's behavior was constantly changing and strategies that would not adapt to these changes, would become obsolete, and their performance would thus decrease over time. However, because experiments in that work were based on a specific GP algorithm, we are interested in this paper to prove that those results are independent of the choice of such algorithms. We thus repeat our previous tests under two more GP frameworks. In addition, while our previous work surveyed only a single market, in this paper we run tests under 10 markets, for generalization purposes. Finally, we deepen our analysis and investigate whether the performance of strategies, which have not co-evolved with the market, follows a continuous decrease, as it has been previously suggested in the agent-based artificial stock market literature. Results show that our previous results are not sensitive to the choice of GP. Strategies that do not co-evolve with the market, become ineffective. However, we do not find evidence for a continuous performance decrease of these strategies. I. INTRODUCTIONThere are several types of models in the agent-based financial markets literature. One way of categorizing them is to divide them into the N -type models and the Santa-Fe Institute (SFI) like ones (see [1] for details). The former type of models focuses on the mesoscopic level of markets, by allowing agents to choose among different types of strategies. A typical example is the fundamentalist-chartists model. Agents in this model are presented with these two strategy types and at any given time they have to choose between these two. Other examples of such N -type models in the literature are [2], [3], [4]. A typical area of investigation of these models is fraction dynamics, i.e. how the fractions of the different strategy types change over time. However, what is not presented in most of these models is novelty-discovering agents. For instance, in the fundamentalist-chartists example, agents can only choose between these two types; they cannot create new strategies that do not fall into these two types. On the other hand,

show abstract

“…Like El-Gamal and Grether (1995), these approaches have in common that the set of possible subject behaviors must be pre-specified by the researcher. Recently, Duffy and Engle-Warnick (2001) and Engle-Warnick (2003), proposed a procedure that models decision rules as sequences of nested if-then conditions. This procedure is, in principle, more flexible.…”

mentioning

confidence: 99%

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Houser¹,

Keane²,

McCabe³

2004

Econometrica

154

View full text Add to dashboard Cite

Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label "Near Rational," "Fatalist," and "Confused." There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.

show abstract

Using Symbolic Regression to Infer Strategies from Experimental Data

Cited by 40 publications

References 2 publications

Distilling Free-Form Natural Laws from Experimental Data

Distilling Free-Form Natural Laws from Experimental Data

Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Contact Info

Product

Resources

About