Universe 2738 from The Wolfram Physics Project

4 steps

wm2738`wm2738`

signature

1₄→4₄

rule

{{{1, 2, 3, 3}} -> {{4, 1, 1, 1}, {1, 5, 4, 4}, {1, 4, 2, 6}, {5, 4, 3, 2}}}

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Basic Evolution

Basic evolution:

[◼]	WolframModel

[{{{1,2,3,3}}{{4,1,1,1},{1,5,4,4},{1,4,2,6},{5,4,3,2}}},{{1,1,1,1}},4,"StatesPlotsList"]

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

Event-by-event evolution:

[◼]	WolframModel

[{{{1,2,3,3}}{{4,1,1,1},{1,5,4,4},{1,4,2,6},{5,4,3,2}}},{{1,1,1,1}},<|"MaxEvents"6|>,"EventsStatesPlotsList"]

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

Vertex and edge counts:

{vertexCountList,edgeCountList}=

[◼]	WolframModel

[{{{1,2,3,3}}{{4,1,1,1},{1,5,4,4},{1,4,2,6},{5,4,3,2}}},{{1,1,1,1}},11,{"VertexCountList","EdgeCountList"}];

ListLogPlot{vertexCountList,edgeCountList},



	vertices
	edges

Symbolic expression for vertex count:

FindSequenceFunction[vertexCountList,t]

8+8

(1-

)

(1-

)

-6

(1+

)

-3

(1+

)

4(1+

)

Symbolic expression for edge count:

FindSequenceFunction[edgeCountList,t]

8+8

(1-

)

(1-

)

-6

(1+

)

-3

(1+

)

4(1+

)

Result after

generations:

WolframModel[

]

["FinalStatePlot"]

Causal Graph

Causal graph:

WolframModel[

]

"CausalGraph",

Rule[

]



Layered rendering:

WolframModel[

]

["LayeredCausalGraph"]

Causal graph distance matrix:

MatrixPlotTransposeGraphDistanceMatrix

WolframModel[

]

["CausalGraph"],



Final State Properties

Hypergraph adjacency matrix:

MatrixPlotAdjacencyMatrix@CatenateMapUndirectedEdge@@@Subsets[#,{2}]&,

WolframModel[

]

["FinalState"],



Vertex degree distribution:

HistogramValuesCountsCatenateUnion/@

WolframModel[

]

["FinalState"],



Neighborhood volumes (ignoring directedness of connections):

volumes=

[◼]	RaggedMeanAround

Values

[◼]	HypergraphNeighborhoodVolumes



WolframModel[

]

["FinalState"],All,Automatic;

ListLogLogPlotvolumes,



Effective dimension versus radius:

ListLinePlot

[◼]	LogDifferences

[volumes],



Successive neighborhood balls around a random vertex:

[◼]	HypergraphNeighborhoods



WolframModel[

]

["FinalState"],4

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

Distance matrix:

distanceMatrix=GraphDistanceMatrixUndirectedGraph

[◼]	HypergraphToGraph



WolframModel[

]

["FinalState"];

MatrixPlotExp[-(distanceMatrix/.0None)],



Distribution of distances in the graph:

HistogramFlatten[distanceMatrix],



Spreading of Effects

Causal graph adjacency matrix:

MatrixPlotAdjacencyMatrix

WolframModel[

]

["CausalGraph"],



Neighborhood volumes in causal graph:

ListLogLogPlotValues

[◼]	GraphNeighborhoodVolumes



WolframModel[

]

["CausalGraph"],{1},



Other Evolution Orders

Random evolutions:

[◼]	WolframModel

[{{{1,2,3,3}}{{4,1,1,1},{1,5,4,4},{1,4,2,6},{5,4,3,2}}},{{1,1,1,1}},<|"MaxEvents"28|>,"FinalStatePlot","EventOrderingFunction""Random"]

Different deterministic evolution orders:

[◼]	WolframModel

[{{{1,2,3,3}}{{4,1,1,1},{1,5,4,4},{1,4,2,6},{5,4,3,2}}},{{1,1,1,1}},<|"MaxEvents"28|>,"EventOrderingFunction"{#,"LeastRecentEdge","RuleOrdering","RuleIndex"}]["FinalStatePlot",PlotLabel#]&/@{"OldestEdge","LeastOldEdge","LeastRecentEdge","NewestEdge","RuleOrdering","ReverseRuleOrdering"}

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