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SIMPLE DETERMINISTIC FREE WILL
from May 16, 2002 until November 6, 2005
A common feature of free will is that a person has choices among
alternative actions and chooses the action with the apparently most
preferred consequences. In a determinist theory, the mechanism that
makes the choice among the alternatives is determinist. The sensa-
tion of free will comes from the fact that the mechanism that gen-
erates the choices uses a non-determinist theory as a computational
device and that the stage in which the choices have been identiﬁed is
introspectable. The present formalism is based on work in artiﬁcial
We present a theory of simple deterministic free will (SDFW) in a
deterministic world. The theory splits the mechanism that determines
action into two parts. The ﬁrst part computes possible actions and
their consequences. Then the second part decides which action is most
preferable and does it.
We formalize SDFW by two sentences in situation calculus, a math-
ematical logical theory often used in AI. The situation calculus for-
malization makes the notion of free will technical. According to this
notion, almost no animal behavior exhibits free will, because exer-
cising free will involves considering the consequences of alternative
actions. A major advantage of our notion of free will is that whether
an animal does have free will may be determinable by experiment.
Some computer programs, e.g. chess programs, exhibit SDFW. Al-
most all do not. At least SDFW seems to be required for eﬀective
chess performance and also for human-level AI.
Many features usually considered as properties of free will are omit-
ted in SDFW. That’s what makes it simple. The criterion for whether
an entity uses SDFW is not behavioristic but is expressed in terms of
the internal structure of the entity.
1 The Informal theory
Let the course of events, including events in my brain (or yours or his or its)
be deterministic. It seems to many people that there is no place for free will.
Even our thoughts are determined.
However, if we examine closely how a human brain (or chess program) de-
terministically makes decisions, free will (or imitation free will if your philos-
ophy forbids calling it real free will) must come back in. Some deterministic
processes consider alternative actions and their consequences and choose the
actions they think have the most preferred consequences. This deterministic
decision process uses a nondeterministic theory to present the set of available
actions and the consequences of each of them.
When a person, animal, or machine reacts directly to a situation rather
than comparing the consequences of alternative actions, free will is not in-
volved. So far as I can see, no animals consider the consequences of al-
ternative actions; hence they don’t have free will. Others think that apes
sometimes do compare consequences. A relevant experiment is suggested in
section 7. Using free will is too slow in many situations, and training and
practice often have the purpose of replacing comparison of consequences by
automatic reaction to a situation.
We believe this simple theory covers the most basic phenomenon of human
free will. We’ll call it simple deterministic free will and abbreviate it SDFW.
Robots with human-level intelligence will also require at least this much free
will in order to be useful.
Beyond having free will, some systems are conscious of having free will
and communicate about it. If asked to tell what it is doing, humans or some
machine will tell about their choices for action and say that they intend to de-
termine which action leads to the best consequence. Such a report, whether
given externally or contemplated internally, constitutes the human sensation
and the human report of free will. SDFW does not require consciousness of
having free will or the ability to communicate about it. That’s what’s sim-
ple about SDFW. Thinking about one’s free will requires theoretical struc-
ture beyond or above SDFW. So will considering actions as praiseworthy or
blameworthy. SDFW also doesn’t treat game theoretic situations in which
probabilistic mixed strategies are appropriate.
In AI research one must treat simple cases of phenomena, e.g.
tional behavior, because full generality is beyond the state of the art. Many
philosophers are inclined to only consider the general phenomenon, but this
limits what can be accomplished. I recommend to them the AI approach of
doing the simplest cases ﬁrst.
2 Situation calculus formulas for SDFW
Artiﬁcial intelligence requires expressing this phenomenon formally, and we’ll
do it here in the mathematical logical language of situation calculus. Situ-
ation calculus is described in [MH69], [Sha97], [Rei01], and in the extended
form used here, in [McC02]. Richmond Thomason in [Tho03] compares situ-
ation calculus to theories of action in the philosophical literature. As usually
presented, situation calculus is a non-deterministic theory. The equation
s0 = Result(e, s)
asserts that s0 is the situation that results when event e occurs in the situation
s. Since there may be many diﬀerent events that can occur in s, and the
theory of the function Result does not say which occurs, the theory is non-
deterministic. Some AI jargon refers to it as a theory with branching time
rather than linear time. Actions are a special case of events, but most AI
work discusses only actions.
Usually, there are some preconditions for the event to occur, and then we
have the formula
recond(e, s) → s0 = Result(e, s).
[McC02] proposes adding a formula Occurs(e, s) to the language that can
be used to assert that the event e occurs in situation s. We have
Occurs(e, s) → (N
ext(s) = Result(e, s)).
Adding occurrence axioms, which assert that certain actions occur, makes
a theory more deterministic by specifying that certain events occur in situa-
tions satisfying speciﬁed conditions. In general the theory will remain partly
non-deterministic, but if there are occurrence axioms specifying what events
occur in all situations, then the theory becomes deterministic, i.e. has linear
We can now give a situation calculus theory for SDFW illustrating the
role of a non-deterministic theory in determining what will deterministically
happen, i.e. by saying what choice a person or machine will make.
In these formulas, lower case terms denote variables and capitalized terms
denote constants. Suppose that actor has a choice of just two actions a1
and a2 that he may perform in situation s. We want to say that the
event Does(actor, a1) or Does(actor, a2) occurs in s according to which of
Result(Does(actor, a1), s) or Result(Does(actor, a2), s) actor prefers.
The formulas that assert that a person (actor) will do the action that he,
she or it thinks results in the better situation for him are
Occurs(Does(actor, Choose(actor, a1, a2, s), s), s),
Choose(actor, a1, a2, s) =
if P ref ers(actor, Result(a1, s), Result(a2, s))
then a1 else a2.
Adding (2) makes the theory determinist by specifying which choice us
Here Prefers(actor, s1, s2) is to be understood as asserting that actor
prefers s1 to s2.
Here’s a non-deterministic theory of greedy John.
Result(A1, S0) = S1,
Result(A2, S0) = S2,
W ealth(J ohn, S1) = $2.0 × 106,
W ealth(J ohn, S2) = $1.0 × 106,
(∀s s0)(W ealth(J ohn, s) > W ealth(J ohn, s0)
→ Prefers(J ohn, s, s0).
As we see, greedy John has a choice of at least two actions in situation S0
and prefers a situation in which he has greater wealth to one in which he has
From equations 1-3 we can infer
Occurs(Does(J ohn, A1, S0)).
1(2) uses a conditional expression. if p then a else b has the value a if the proposition
p is true and otherwise has the value b. The theory of conditional expressions is discussed
in [McC63]. Conditional expressions are used in the Lisp, Algol 60, Algol 68, and Scheme
For simplicity, we have omitted the axioms asserting that A1 and A2 are
exactly the actions available and the nonmonotonic reasoning used to derive
Here Prefers(actor, s1, s2) is to be understood as asserting that actor
prefers s1 to s2.
I used just two actions to keep the formula for Choose
short. Having more actions or even making Result probabilistic or quantum
would not change the nature of SDFW. A substantial theory of Prefers is
beyond the scope of this article.
This illustrates the role of the non-deterministic theory of Result within
a deterministic theory of what occurs. (1) includes the non-deterministic of
Result used to compute which action leads to the better situation. (2) is the
deterministic part that tells which action occurs.
We make four claims.
action rules. Chess programs always have.
approximate situations the agent uses in making its decisions. [McC00] has
a discussion of approximate entities. Part of the problem of building human-
level AI lies in inventing what kind of entity Result(a, s) shall be taken to
Formulas (1) and (2) illustrate person making a choice. They don’t say
anything about person knowing it has choices or preferring situations in
which it has more choices. SDFW is therefore a partial theory that requires
extension when we need to account for these phenomena.
3 A generalization of SDFW
We can generalize SDFW by applying preferences to actions rather than to
the situations resulting from actions. The formulas then become
action(actor, a1, a2, s), s), s)
action(actor, a1, a2, s) =
if P ref ers-
action(actor, a1, a2, s)
then a1 else a2.
(5) and (6) obviously generalize (1) and (2), because the earlier case is
obtained by writing
P ref ers-
action(a1, a2, s) ≡ P ref ers(Result(a, s), Result(a2, s)).
I am doubtful about the generalization, because I don’t see how to repre-
sent commonsense preferences between actions except in terms of preferring
one resulting situation to another.
4 Knowledge of one’s free will and wanting
more or fewer choices
This section is less worked out than basic SDFW and not axiomatized. That’s
why it was best to start simple.
Here are some examples of it being good to have more choices.
“I’ll take my car to work today rather than bicycling so I can shop on the
way home if I want to.”
“If you learn mathematics, you will more choice of scientiﬁc occupations”.
“The more money I have, the more models of car I can choose from.”
“If I escape from Cuba, I will have more choice of what to read, what I
can say or write, and where to travel.”
We want to say that situation s1 is at least as free as situation s2, written
s1 ≥f reedom s2, if every ﬂuent achievable by a single action from s2 is achiev-
able from s1. Just as with equation (1), we can say that person chooses an
action that leads to more freedom at the next situation.
s1 ≥f reedom s2
(∀f )((∃a)(Holds(f, Result(Does(person, a), s2)))
(∃a)(Holds(f, Result(Does(person, a), s1)))).
Here f ranges over ﬂuents. Having more choices is usually preferred.
However, one sometimes wants fewer choices. Burning one’s bridges, nailing
the ﬂag to the mast, and promising to love until death do us part are examples
of actions that reduce choices. The conditions under which this occurs are too
diﬃcult for me to formalize at present. They can involve fearing that one’s
preferences in the future might be diﬀerent from one’s present preferences
for future actions or that making a commitment about one’s future actions
confers a present beneﬁt.
5 Philosophical issues
The formalism of this paper takes sides in several philosophical controversies.
all complications be understood before anything can be said. In this it
resembles the approaches to belief and other intentional states discussed
in [Den71], [Den78], and [McC79]. Starting with simple systems is the
practice in AI, because only what is understood can be implemented
in computer programs.
It seems to me that formulas (1) and (2) expressing the use of the branch-
ing time Result(e, s) function in determining what events occur make the
philosophical ideas deﬁnite. Thus we can see which modiﬁcations of the
notions are compatible with (1) and (2), and which require diﬀerent axioms.
The process of deciding what to do often involves considering a pruned
set of actions which eliminate those that have obviously bad consequences.
The remaining actions are those that one can do. When someone refers to
a pruned action, one sometimes gets the reply, “Oh, I could do that, but I
really can’t, because . . . .”
6 Praise and blame
We have maintained that the basic notion of free will is the same for humans,
animals and robots. Praising or blaming agents for their actions is an ad-
vanced notion requiring more structure, e.g. including good or bad actions or
outcomes. Blaming or praising humans requires taking human peculiarities,
not shared with agents in general, e.g. robots, into account.
Consider the verdict: “Not guilty by reason of insanity” as applied to a
person with schizophrenia. Schizophrenia is basically a disease of the chem-
istry of the blood and nervous system. At a higher level of abstraction, it is
regarded as a disease in which certain kinds of thoughts enter and dominate
consciousness. A patient’s belief that the CIA has planted a radio in his brain
is relieved by medicines that change blood chemistry. If the patient’s belief
caused him to kill someone whom he imagined to be a CIA agent, he would
be found not guilty by reason of insanity. If we wanted robots susceptible
to schizophrenia, we would have to program in something like schizophrenia,
and it would be a complicated and unmotivated undertaking—unmotivated
by anything but the goal of imitating human schizophrenia. The older Mc-
Nachten criterion, “unable to understand the nature and consequences of
his acts”, uses essentially the criteria of the present article for assessing the
presence or absence of free will.
I don’t know if all praise or blame for robots is artiﬁcial; the matter
requires more thought. Verbally one might praise a robot as way of getting
it to do more of the same.
7 A possible experiment with apes
Here’s a gedanken experiment aimed at determining whether apes (or other
animals) have free will in the sense of this article. The criterion is whether
they consider the consequences of alternate actions.
The ape can move a lever either to the left or the right. The lever causes
a prize to be pushed oﬀ a shelf, either to the left or the right. The goody
then hits a baﬄe and is deﬂected either to the ape in control of the lever or
to a rival ape. On each trial, the baﬄe is set by the experimenter. The whole
apparatus is visible to the ape, so it can see the consequences of each choice.
The free will involves the ape having two choices and being able to de-
termine the consequences of each choice.
There is a possibility that the ape can win without determining the conse-
quences of the possible actions. It may just learn a rule relating the position
of the baﬄe and the action that will get the prize. Maybe we wouldn’t be
able to tell whether the ape predicted the consequences or not.
We can elaborate the experiment to obviate this diﬃculty. Let there be a
sequence of (say) six baﬄes that are put in a randomly selected conﬁguration
by the experimenter or his program at each trial. Each baﬄe deﬂects the
prize one way or the other according to how it is set. If the ape can mentally
follow the prize as it would bounce from baﬄe to baﬄe, it will succeed.
However, there are 64 combinations of baﬄe positions. If a training set of
(say) 32 combinations permits the ape to do the remaining 32 without further
trial and error, it would be reasonable to conclude that the ape can predict
the eﬀects of the successive bounces.
I hope someone who works with apes will try this or a similar experiment.
Frogs are simpler than apes. Suppose a frog sees two ﬂies and can stick out
its tongue to capture one or the other. My prejudice is that the frog doesn’t
consider the consequences of capturing each of the two ﬂies but reacts directly
to its sensory inputs. My prejudice might be refuted by a physiological
Suppose ﬁrst that frogs can taste ﬂies, i.e. when a frog has a ﬂy in its
mouth, an area of the frog’s brain becomes active in a way that depends
on the kind of ﬂy. Suppose further that when a frog sees a ﬂy, this area
becomes active, perhaps weakly, in the same way as when the frog has the
ﬂy in its mouth. We can interpret this as the frog imagining the taste of
the ﬂy that it sees. Now further suppose that when the frog sees two ﬂies, it
successively imagines their tastes and chooses one or the other in a consistent
way depending on the taste. If all this were demonstrated, I would give up
my prejudice that frogs don’t have SDFW.
8 Comparison with Dennett’s ideas
Daniel Dennett [Den03] writes about The evolution of freedom. I agree with
him that free will is a result of evolution. It may be based on a more basic
ability to predict something about what future will result from the occurrence
of certain events including actions. He compares determinism and inevitabil-
ity, and makes deﬁnitions so that in a deterministic world, not all events that
occur are inevitable. He considers that freedom evolves in such a way as to
make more and more events evitable, especially events that are bad for the
Dennett’s ideas and those of this paper are in the same direction and
somewhat overlap. I think SDFW is simpler, catches the intuitive concepts
of freedom and free will better, and are of more potential utility in AI.
Consider a species of animal with eyes but without a blink reﬂex. Every
so often the animal will be hit in the eye and suﬀer an injured cornea. Now
suppose the species evolves a blink reﬂex. Getting hit in the eye is now often
evitable in Dennett’s sense. However, it is not an exercise of free will in
my sense.2 On the other hand, deciding whether or not to go through some
bushes where there was a danger of getting hit in the eye on the basis of
weighing the advantages against the dangers would be an exercise of free will
in my sense. It would also be an evitability in Dennett’s sense.
Evitability assumes that there is a normal course of events some of which
may be avoided, e.g. that getting hit in they eye is normal and is avoided
by the blink reﬂex. My notion of free will does not involve this, because the
choice between actions a1 and a2 is symmetric. It is interesting to ask when
there are normal events that can sometimes be avoided.
The converse of an evitability is an opportunity. Both depend on a dis-
tinction between an action and non-action. In the case of non-action, nature
takes its course.
9 Summary and remarks
A system operating only with situation-action rules in which an action in
a situation is determined directly from the characteristics of the situation
does not involve free will. Much human action and almost all animal action
reacts directly to the present situation and does not involve anticipating the
consequences of alternative actions.
One of the eﬀects of practicing an action is to remove deliberate choice
from the computation and to respond immediately to the stimulus. This is
often, but not always, appropriate.
Human free will, i.e. considering the consequences of action, is surely the
product of evolution.
free will in his sense
2Dennett (email of 2003 Feb 27) tells me that the blink reﬂex involves no signiﬁcant
Do animals, even apes, ever make decisions based on comparing antici-
pated consequences? Almost always no. Thus when a frog sees a ﬂy and
ﬂicks out its tongue to catch it, the frog is not comparing the consequences
of catching the ﬂy with the consequences of not catching the ﬂy.
One computer scientist claims that dogs (at least his dog) consider the
consequences of alternate actions. I’ll bet the proposition can be tested, but
I don’t yet see how.
According to Dennett (phone conversation), some recent experiments sug-
gest that apes sometimes consider the consequences of alternate actions. If
so, they have free will in the sense of this article.
If not even apes ordinarily compare consequences, maybe apes can be
trained to do it.
Chess programs do compare the consequences of various moves, and so
have free will in the sense of this article. Present programs are not conscious
of their free will, however. [McC96] discusses what consciousness computer
People and chess programs carry thinking about choice beyond the ﬁrst
level. Thus “If I make this move, my opponent (or nature regarded as an
opponent) will have the following choices, each of which will give me further
choices.” Examining such trees of possibilities is an aspect of free will in the
world, but the simplest form of free will in a deterministic world does not
involve branching more than once.
Daniel Dennett [Den78] and [Den03] argue that a system having free will
depends on it being complex. I don’t agree, and it would be interesting to
design the simplest possible system exhibiting deterministic free will. A pro-
gram for tic-tac-toe is simpler than a chess program, but the usual program
does consider choices.
However, the number of possible tic-tac-toe positions is small enough so
that one could make a program with the same external behavior that just
looked up each position in a table to determine its move. Such a program
would not have SDFW. Likewise, Ken Thompson has built chess programs
for end games with ﬁve or fewer pieces on the board that use table lookup
rather than look-ahead. See [Tho86]. Thus whether a system has SDFW
depends on its structure and not just on its behavior. Beyond 5 pieces,
direct lookup in chess is infeasible, and all present chess programs for the
full game use look-ahead, i.e. they consider alternatives for themselves and
their opponents. I’ll conjecture that successful chess programs must have at
least SDFW. This is not the only matter in which quantitative considerations
make a philosophical diﬀerence. Thus whether the translation of a text is
indeterminate depends on the length of the text.
Simpler systems than tic-tac-toe programs with SDFW are readily con-
structed. The theory of greedy John formalized by (3) may be about as
simple as possible and still involves free will.
Essential to having any kind of free will is knowledge of one’s choices of
action and choosing among them. In many environments, animals with at
least SDFW are more likely to survive than those without it. This seems to
be why human free will evolved. When and how it evolved, as with other
questions about evolution, won’t be easy to answer.
Gary Drescher [Dre91] contrasts situation-action laws with what he calls
the prediction-value paradigm. His prediction-value paradigm corresponds
approximately to the deterministic free will discussed in this article.
I thank Drescher for showing me his forthcoming [Dre06]. His notion of
choice system corresponds pretty well to SDFW, although it is imbedded in
a more elaborate context.
This article beneﬁted from discussions with Johan van Benthem, Daniel
Dennett, Gary Drescher, and Jon Perry. The work was partly supported by
the Defense Advanced Research Projects Agency (DARPA).
[Den71] Daniel C. Dennett. Intentional systems. The Journal of Philosophy,
[Den78] Daniel Dennett. Brainstorms: Philosophical Essays on Mind and
Psychology. Bradford Books/MIT Press, Cambridge, 1978.
[Den03] Daniel Dennett. Freedom Evolves. Viking, 2003.
[Dre91] Gary Drescher. Made up minds: a constructivist approach to arti-
ﬁcial intelligence. MIT Press, 1991. Q335D724.
[Dre06] Gary Drescher. Good and real: Paradoxes from physics to ethics.
M.I.T. Press, forthcoming, 2006.
[McC63] John McCarthy. A Basis for a Mathematical Theory of Compu-
tation3. In P. Braﬀort and D. Hirschberg, editors, Computer Pro-
gramming and Formal Systems, pages 33–70. North-Holland, Ams-
[McC79] John McCarthy. Ascribing mental qualities to machines4. In Martin
Ringle, editor, Philosophical Perspectives in Artiﬁcial Intelligence.
Harvester Press, 1979. Reprinted in [McC90].
[McC90] John McCarthy. Formalizing Common Sense: Papers by John Mc-
Carthy. Ablex Publishing Corporation, 1990.
[McC96] John McCarthy. Making Robots Conscious of their Mental States5.
In Stephen Muggleton, editor, Machine Intelligence 15. Oxford Uni-
versity Press, 1996. Appeared in 2000. The web version is improved
from that presented at Machine Intelligence 15 in 1995.
[McC00] John McCarthy. Approximate objects and approximate theories6.
In Anthony G. Cohn, Fausto Giunchiglia, and Bart Selman, edi-
tors, KR2000: Principles of Knowledge Representation and Rea-
soning,Proceedings of the Seventh International conference, pages
519–526. Morgan-Kaufman, 2000.
[McC02] John McCarthy. Actions and other events in situation calculus7. In
B. Selman A.G. Cohn, F. Giunchiglia, editor, Principles of knowl-
edge representation and reasoning: Proceedings of the eighth inter-
national conference (KR2002). Morgan-Kaufmann, 2002.
[MH69] John McCarthy and Patrick J. Hayes. Some Philosophical Prob-
lems from the Standpoint of Artiﬁcial Intelligence8. In B. Meltzer
and D. Michie, editors, Machine Intelligence 4, pages 463–502. Ed-
inburgh University Press, 1969. Reprinted in [McC90].
[Rei01] Raymond Reiter. Knowledge in Action. M.I.T. Press, 2001.
[Sha97] Murray Shanahan. Solving the Frame Problem, a mathematical
investigation of the common sense law of inertia. M.I.T. Press,
[Tho86] K. Thompson. Retrograde analysis of certain endgames.
(International Computer Chess Association) Journal, 9(3):131–139,
[Tho03] Richmond Thomason. Logic and artiﬁcial intelligence.
ward N. Zalta, editor, The Stanford Encyclopedia of Philosophy.
/@steam.stanford.edu:/u/ftp/jmc/freewill2.tex: begun Thu May 16, 2002, latexed November 6, 2005